Marina Soraia Gomes de Oliveira - Universidade do Minho · em frente, vivendo dias iguais de forma...

outubro de 2013

Universidade do MinhoEscola de Engenharia

Marina Soraia Gomes de Oliveira

Edible coatings on frozen fish: Effect of applying a chitosan-based coating on the quality of frozen salmon

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Dissertação de Mestrado Mestrado Integrado em Engenharia Biológica Ramo Tecnologia Química e Alimentar

Trabalho realizado sob a orientação do Professor Doutor Engenheiro António Augusto Martins de Oliveira Soares Vicente e doEngenheiro Nuno Miguel Ferreira Soares

outubro de 2013

Universidade do MinhoEscola de Engenharia

Marina Soraia Gomes de Oliveira


iii

Dedicatória

“Não sei se estou perto ou longe demais, sei apenas que sigo

em frente, vivendo dias iguais de forma diferente. Levo comigo

cada recordação, cada vivência, cada lição. E mesmo que tudo

não ande da forma que eu gostaria, saber que já não sou a

mesma de ontem me faz perceber que valeu a pena. Há um

tempo em que é preciso abandonar (…) e esquecer os nossos

caminhos que nos levam sempre aos mesmos lugares… É o

tempo da travessia… e, se não ousarmos fazê-la, teremos

ficado, para sempre, à margem de nós mesmos.”

Fernando Teixeira de Andrade

À minha família

v

Agradecimentos

À família e amigos pelo amor incondicional, em especial aos insubstituíveis: pais, irmãos,

cunhado, padrinho, Margarida, Francisco, Ana, Teresa, Raquel, Filipa e G.J.E..

Ao Engenheiro Nuno Soares pela presença assídua, pelo apoio e companheirismo

constantes e pela irrepreensível e generosa parceria.

Ao Professor Vicente por todo o amparo, pela simpatia e otimismo sempre presentes.

Ao Departamento de Engenharia Biológica, nas pessoas que nele trabalham, em especial

ao pessoal do Laboratório de Instrumentação e Processo e à Plataforma de Microscopia, na pessoa

da Doutora Ana Nicolau, pela paciência e cooperação.

À Vanibru pela disponibilização das instalações e equipamentos e pela hospitalidade dos

seus trabalhadores.

À empresa Castro Pinto & Costa (LabMaia) pela disponibilização do equipamento de HPLC

e pelo fantástico acolhimento e boa disposição permanente, em especial à Engenheira Inês, Filipa

e Elizabete, ao João e à Vânia.

Ao IPMA, na pessoa da Doutora Helena Lourenço, pela partilha de conhecimento na

aplicação da Norma Portuguesa 3356:2009.

À empresa Konica Minolta/Aquateknica, na pessoa do Físico David Roldán, pelo oportuno

esclarecimento na avaliação da cor.

À Tânia, ao Tiago e à Cristina pela atenciosa disponibilidade e gentil companhia.

À Engenheira Susana pelo desafiante convite e pela oportunidade tão bem-vinda de

aprendizagem na superfície comercial MercAtlas.

A todos aqueles que direta ou indiretamente colaboraram comigo para que a realização

deste trabalho fosse possível, o meu mais sincero OBRIGADA.

vii

Revestimentos edíveis em pescado congelado:

Efeito da aplicação de um revestimento à base de quitosano na qualidade de salmão congelado

Resumo

O aumento do consumo de peixe, devido às suas características nutricionais, obrigou a

uma dinamização da indústria do pescado, no que respeita ao melhoramento dos processos de

conservação do mesmo. A congelação e a vidragem são técnicas comumente usadas na redução

da incidência dos processos de deterioração no pescado. Com o objetivo de encontrar uma

alternativa que complementasse a congelação e substituísse o vidrado de água, o presente trabalho

visou avaliar o efeito da aplicação de revestimentos edíveis de 0.5% e 1.5% de quitosano na

qualidade do pescado congelado. Ambos os revestimentos – o vidrado de água e os revestimentos

de quitosano – foram aplicados diretamente em salmão do Atlântico (Salmo salar) congelado e

armazenado durante 6 meses a -22 °C. Comparando ambos os revestimentos entre si e com

amostras controlo não revestidas, diversos parâmetros como perda de revestimento/vidrado, perda

de peso, perda por gotejamento, TVC, TBA, TVB-N, K-value, pH e coordenadas de cor L*a*b* foram

periodicamente avaliados. Encontraram-se resultados favoráveis para as amostras de salmão

revestidas com 0.5% de quitosano no controlo da perda de revestimento e para as amostras

revestidas com 1.5% de quitosano na manutenção da cor do salmão e no controlo da

contaminação microbiana de amostras congeladas e descongeladas. Neste trabalho vários

parâmetros, como a perda de revestimento/vidrado, perda de peso, perda por gotejamento, TVC,

TBA, TVB-N e K-value, revelaram-se bastante estáveis devido à proteção providenciada por uma

correta temperatura de armazenamento e por um controlo apropriado da sua manutenção.

Palavras-chave: salmão, qualidade, congelação, vidragem, quitosano.

ix

Edible coatings on frozen fish:

Effect of applying a chitosan-based coating on the quality of frozen salmon

Abstract

The increase of fish consumption due to its nutritional characteristics, led to a stimulation

of fishing industry, as regards the improvement of the processes for its conservation. Freezing and

glazing are techniques commonly used in reducing the incidence of fish deterioration processes. In

order to find an alternative to complement freezing and replace water glaze, the present work aimed

at evaluating the effect of edible coatings of 0.5% and 1.5% chitosan on the quality of frozen fish.

Both coatings - water glazing and chitosan coatings - were applied directly on Atlantic salmon

(Salmo salar) frozen and stored for 6 months at -22 °C. Comparing both coatings with each other

and with control uncoated samples, several parameters such as coating/glazing loss, weight loss,

drip loss, TVC, TBA, TVB-N, K-value, pH and color coordinates L*a*b* were periodically evaluated.

Favorable results were found for salmon samples coated with 0.5% chitosan in the control of

coating loss and for the samples coated with 1.5% chitosan in maintaining the color of the salmon

and controlling microbial contamination of samples frozen and thawed. In this work several

parameters, such as coating loss, weight loss, drip loss, TVC, TBA, TVB-N, and K-value maintained

quite stable due to the protection provided by a correct freezing temperature and a suitable control

of its maintenance.

Keywords: salmon, quality, freezing, glazing, chitosan.

xi

List of Contents

Dedicatória ................................................................................................................................ iii

Agradecimentos .......................................................................................................................... v

Resumo .................................................................................................................................... vii

Abstract .................................................................................................................................... ix

List of Nomenclature .................................................................................................................. xv

List of Figures ........................................................................................................................... xix

List of Tables ...........................................................................................................................xxiii

Introduction ................................................................................................................................ 1

Part I – State of Art...................................................................................................................... 3

Chapter 1. Fish........................................................................................................................... 5

1.1. Fish Industry .............................................................................................................. 6

1.2. Fish - Chemical composition and structure .................................................................. 7

1.3. Fish Conservation......................................................................................................10

1.3.1. Freezing ............................................................................................................10

1.3.2. Glazing .............................................................................................................12

1.3.3. Edible coatings/films .........................................................................................13

1.3.3.1. Chitosan .......................................................................................................14

Chapter 2. Fish Quality - Freshness ............................................................................................ 17

2.1. Microbiology ..................................................................................................................17

2.1.1. Total Viable Counts (TVC) ........................................................................................17

2.2. Lipids ............................................................................................................................20

2.2.1. 2-Thiobarbituric acid (TBA) ......................................................................................20

2.3. Volatiles ........................................................................................................................22

2.3.1. Total Volatile Basic Nitrogen (TVB-N) ........................................................................22

2.4. Adenosine-5’-triphosphate (ATP) .....................................................................................23

2.4.1. K-value ...................................................................................................................23

2.5. pH ................................................................................................................................25

xii

2.6. Sensory Analysis ............................................................................................................ 26

2.6.1. Color ...................................................................................................................... 26

2.7. Physical properties ......................................................................................................... 33

2.7.1. Temperature and storage time ................................................................................. 33

Part II – Experimental Work ........................................................................................................ 37

Chapter 3. Materials and Methods ............................................................................................. 39

3.1. Fish preparation ............................................................................................................ 39

3.2. Preparation of the coating solutions ................................................................................ 39

3.3. Preparation of the samples ............................................................................................. 40

3.3.1. Preparation of the samples coated with chitosan....................................................... 40

3.3.2. Preparation of the samples glazed with water............................................................ 41

3.3.3. Preparation of the control samples ........................................................................... 41

3.4. Storage and transport of the samples.............................................................................. 41

3.5. Samples Analysis ........................................................................................................... 42

3.5.1. Coating Loss........................................................................................................... 42

3.5.2. Glazing Loss ........................................................................................................... 42

3.5.3. Weight Loss ............................................................................................................ 42

3.5.4. Drip Loss ................................................................................................................ 42

3.5.5. Determination of TVC .............................................................................................. 43

3.5.6. Determination of TBA .............................................................................................. 45

3.5.7. Determination of TVB-N ........................................................................................... 46

3.5.8. Determination of K-value ......................................................................................... 47

3.5.9. Determination of pH-value ....................................................................................... 48

3.5.10. Determination of color parameters ......................................................................... 49

3.5.11. Statistical analysis ................................................................................................. 50

Chapter 4. Results and Discussion ............................................................................................ 51

4.1. Coating Loss ................................................................................................................. 51

xiii

4.2. Weight Loss ...................................................................................................................53

4.3. Drip Loss.......................................................................................................................54

4.4. TVC...............................................................................................................................55

4.5. TBA...............................................................................................................................57

4.6. TVB-N ...........................................................................................................................59

4.7. K-value ..........................................................................................................................60

4.8. pH-value........................................................................................................................61

4.9. Color parameters ...........................................................................................................62

Chapter 5. Conclusions and future perspectives ......................................................................... 67

References ............................................................................................................................... 71

Appendixes ............................................................................................................................... 75

A1 – Standard Curve Determination for TBA method ...............................................................75

A2 – Illustrations assistants to the implementation of standard NP2930:2009 ..........................77

A3 – Calibration curves for HPLC determinations ....................................................................79

A4 – Measurement of freezing chamber temperature during storage ........................................83

xv

List of Nomenclature

Abbreviations

Abs - Absorbance

ADP - Adenosine diphosphate

AMP - Adenosine monophosphate

APC - Aerobic Plate Counts

Art. - Article

ATP - Adenosine triphosphate

BS EN ISO - British, European and International Standard Organization

CFU - Colony forming units

Co. Ltd. - Company Limited

DL - Decree - Law

DSC - Differential Scanning Calorimetry

EDTA - Ethylenediaminetetraacetic acid

ESR - Electron Spin Resonance

FAO - Food and Agriculture Organization

FTIR - Fourier Transform Infrared Radiation

HPLC - High Performance Liquid Chromatography

HQL - High-quality life

Hx - Hypoxanthine

HxR - Inosine

ICMSF - International Commission on Microbiological Specifications for Foods

IIR - International Institute of Refrigeration

IMP - Inosine monophosphate

IV - Infra-red

JND - Just Noticeable Difference

K-value - ATP breakdown products

MA - Malonaldehyde

MDA - malondialdehyde

mg MDA/Kg sample - milligrams of malondialdehyde per 1000 g of sample

xvi

mg N/100 g sample – milligrams of nitrogen per 100 g of sample

MRD - Maximum Recovery Diluent

N - Nitrogen

NMR - Nuclear Magnetic Resonance

NP - Portuguese Standard

OSI - Oxidative Stability Instrument

p-AnV - p-anisidine value

PCA - Plate Count Agar

PPP - product-processing-packaging

PSL - Practical Storage Life

PV - Peroxide Value

RGB - Red Green Blue

s seconds

S.A. - Anonymous Society

SD - Standard Deviation

ssp. - specie

TBA - Thiobarbituric Acid

TBA-MDA - Thiobarbituric acid-malondialdehyde complex

TBARS - Thiobarbituric Acid Relative Substance

TCA - Trichloroacetic acid

TMA - Trimethylamine

TTT - Time Temperature Tolerance

TVB-N - Total Volatile Basic Nitrogen

TVC - Total viable counts

UV - Ultraviolet

UV/Vis - Ultraviolet-Visible

Symbols

a* - Red and green direction

aw - water activity

xvii

b* - Yellow and blue direction

C - Concentration of malondialdehyde (μmol)

d - Dilution factor corresponding to the first dilution

∆E*ab - Color difference

∆a* - Difference in the value a* found between the sample color and the color of the standard

∆b* - Difference in the value b* found between the sample color and the color of the standard

∆L* - Difference in the value L* found between the sample color and the color of the standard

Fc - Volume correction factor (moisture of sample)

H - Moisture content of the sample (%)

L* - Lightness

m - Mass of the taking the test (g)

M - Molar mass

ms - Mass of the sample (g)

N - Number of microorganisms

n=3 - Triplicate samples

n1 - Number of dishes retained in the first dilution

n2 - Number of dishes retained in the second dilution

p - Significance

v - Volume of the extract (mL)

V0 - Volume of hydrochloric acid added in the blank test (mL)

V1 - Volume of hydrochloric acid added in the diffusion control test (mL)

V2 - Volume of hydrochloric acid added in the extract test (mL)

V3 - Volume of filtrate added in the periphery of the Conway cell (mL)

W1 - Weight of the salmon sample before the coating application (g)

W2 - Weight of the salmon sample after the coating application (g)

W3 - Weight before the glaze is apply in the samples (g)

W4 - Weight after the glaze is apply in the samples (g)

W5 - Weight of the coated samples after the storage period (g)

W6 - Weight of the glazed samples after the storage period (g)

W7 - Weight of the uncoated samples (g)

W8 - Weight of the uncoated samples after the storage period (g)

W9 - Weight of frozen samples without coating/glazing and before being placed in the refrigerator (g)

xviii

W10 - Weight of thawed samples (g)

[…] - Concentration (μmol/mL)

∑c - Sum of colonies counted

xix

List of Figures

Chapter 1. Fish

Figure 1.1. Representative scheme of internal and external anatomy of salmon. ............................ 8

Figure 1.2. Diagram of fish muscle. ............................................................................................ 9

Figure 1.3. Chemical structure of chitin (a) and chitosan (b). ......................................................15

Chapter 2. Fish Quality – Freshness

Figure 2.1. Relationship between quality and freshness. .............................................................. 18

Figure 2.2. Temperature ranges for different microbial life forms. ................................................. 19

Figure 2.3. Autoxidation of polyunsaturated lipid. ........................................................................ 21

Figure 2.4. TBA test reaction between 2-thiobarbituric acid and malonaldehyde, forming a colored

compound, measured in a spectrophotometer at 530 nm. .......................................................... 21

Figure 2.5. Categorization of fish odours and the volatile compounds that contribute to the

characteristic odour of fresh, spoiled and oxidized fish. ............................................................... 22

Figure 2.6. Aerobic and anaerobic breakdown of glycogen in fish muscle. .................................... 25

Figure 2.7. Electromagnetic and visible spectra. ......................................................................... 28

Figure 2.8. Different objects surfaces modifying the light. ............................................................ 28

Figure 2.9. RGB system – Color’s Additive Primaries. .................................................................. 29

Figure 2.10. Munsell Color System (HSL – the three dimensions of color). ................................... 30

Figure 2.11. Color space (CIE L*a*b*) – Mapping Color’s dimensions. ........................................ 31

Figure 2.12. Illustrative scheme of perceptible versus acceptable differences. ............................... 32

Figure 2.13. Tolerance sphere for acceptable color difference. ..................................................... 33

Chapter 3. Materials and Methods

Figure 3.1. Illustration of the salmon fillet, exemplifying the scheme of cuts used. ........................ 39

Figure 3.2. Pilot-scale glazing tank (left) and dipping instrument – mesh (right) constructed in A151

316 stainless steel. ................................................................................................................... 40

Figure 3.3. Example of serial dilution from an initial sample. ....................................................... 44

xx

Figure 3.4. Representation of the Conway cell. ........................................................................... 46

Figure 3.5. Organizational scheme of the HPLC equipment used. ................................................ 48

Figure 3.6. Illustration of the pH meter and pH range typical for freshwater fish. .......................... 49

Figure 3.7. Illustration of the methodology and equipment used in the measurement of the color of

salmon. ................................................................................................................................... 50

Chapter 4. Results and Discussion

Figure 4.1. Coating Loss (%) for salmon samples glazed with water ( Q0) and coated with 0.5%

chitosan ( Q5) and 1.5% chitosan ( Q15) during 6 months of storage at -22 °C. Each bar

represents the mean ± standard deviation of three replications. Different small letters in the same

day and different capital letters in bars with the same color indicate a statistically significant

difference (Tukey test, p<0.05).................................................................................................. 52

Figure 4.2. Weight Loss (%) of uncoated salmon samples from the control group ( QS) during 6

months of storage at -22 °C. Each bar represents the mean ± standard deviation of three

replications. Different letters indicate a statistically significant difference (Tukey test, p<0.05). ...... 53

Figure 4.3. Drip Loss (%) of salmon samples from the control group ( QS), glazed with water (

Q0) and coated with 0.5% chitosan ( Q5) and 1.5% chitosan ( Q15) during 6 months of storage

at -22 °C. Each bar represents the mean ± standard deviation of three replications. Different small

letters in the same day and different capital letters in bars with the same color indicate a statistically

significant difference (Tukey test, p<0.05). ................................................................................. 55

Figure 4.4. Thiobarbituric acid (TBA) values for salmon samples from the control group ( QS),

glazed with water ( Q0) and coated with 0.5% chitosan ( Q5) and 1.5% chitosan ( Q15) during 5

months of storage at -22 °C. Each bar represents the mean ± standard deviation of three

replications. Different small letters in the same day and different capital letters in bars with the

same color indicate a statistically significant difference (Tukey test, p<0.05). ............................... 58

Figure 4.5. Total volatile basic nitrogen (TVB-N) values for salmon samples from the control group (

QS), glazed with water ( Q0) and coated with 0.5% chitosan ( Q5) and 1.5% chitosan ( Q15)

during 6 months of storage at -22 °C. Each bar represents the mean ± standard deviation of three

replications. Different small letters in the same day and different capital letters in bars with te same

color indicate a statistically significant difference (Tukey test, p<0.05). ........................................ 60

xxi

Figure 4.6. K-values for salmon samples from the control group ( QS), glazed with water ( Q0)

and coated with 0.5% chitosan ( Q5) and 1.5% chitosan ( Q15) during 6 months of storage at -

22 °C. Each bar represents the mean ± standard deviation of three replications. Different small



Figure 4.7. pH values for salmon samples from the control group ( QS), glazed with water ( Q0)

and coated with 0.5% chitosan ( Q5) and 1.5% chitosan ( Q15) during 6 months of storage at -

22 °C. Each bar represents the mean ± standard deviation of three replications. Different small



Figure 4.8. Color parameters for salmon samples from the control group for the coordinates L* (

QSL*), a* ( QSa*) and b* ( QSb*) during 3.5 months of storage at -18 °C. Each bar represents

the mean ± standard deviation of three replications. Different letters in the same color coordinate

indicate a statistically significant difference (Tukey test, p<0.05). ................................................ 64

Figure 4.9. Color parameters for salmon samples from the group glazed with water for the

coordinates L* ( Q0L*), a* ( Q0a*) and b* ( Q0b*) during 3.5 months of storage at -18 °C.

Each bar represents the mean ± standard deviation of three replications. Different letters in the

same color coordinate indicate a statistically significant difference (Tukey test, p<0.05). .............. 64

Figure 4.10. Color parameters for salmon samples from the group coated with 0.5% of chitosan for

the coordinates L* ( Q5L*), a* ( Q5a*) and b* ( Q5b*) during 3.5 months of storage at -18 °C.

Each bar represents the mean ± standard deviation of three replications. Different letters in the


Figure 4.11. Color parameters for salmon samples from the group coated with 1.5% of chitosan for

the coordinates L* ( Q15L*), a* ( Q15a*) and b* ( Q15b*) during 3.5 months of storage at -18

°C. Each bar represents the mean ± standard deviation of three replications. Different letters in the


Figure 4.12. ∆E*ab values for salmon samples from the control group ( QS), glazed with water (

Q0) and coated with 0.5% chitosan ( Q5) and 1.5% chitosan ( Q15) during 3,5 months of

storage at -18 °C...................................................................................................................... 66

xxii

Appendixes

Figure A.1. Color grading obtained on the calibration curve. ........................................................ 75

Figure A.2. Calibration curve for the TBA method. ....................................................................... 76

Figure A.3. Scheme illustrates the preparation of the various tests on Conway cell. ....................... 77

Figure A.4. Representation of the alkalization by the action of potassium carbonate to release

volatile bases and their reception in a boric acid solution followed by titration, in the interior of

Conway cell. ............................................................................................................................ 77

Figure A.5. Calibration curve of IMP. .......................................................................................... 79

Figure A.6. Calibration curve of ATP. .......................................................................................... 80

Figure A.7. Calibration curve of ADP. ......................................................................................... 80

Figure A.8. Calibration curve of Hx. ............................................................................................ 81

Figure A.9. Calibration curve of AMP. ......................................................................................... 81

Figure A.10. Calibration curve of HxR. ........................................................................................ 82

Figure A.11. Air temperature of industrial freezing chamber registered every 10 minutes by a data

logger during frozen storage. ..................................................................................................... 83

Figure A.12. Air temperature of freezing chamber registered every 10 minutes by a data logger

during frozen storage. ............................................................................................................... 83

xxiii

List of Tables


Table 4.1. Total viable counts (TVC) values for frozen salmon samples (-20 °C) from the control

group ( QS), glazed with water ( Q0) and coated with 0.5% chitosan ( Q5) and 1.5% chitosan (

Q15) after 6 months of storage at -22 °C; standard deviation corresponds to three replications . 56

Table 4.2. Total viable counts (TVC) values for refrigerated salmon samples (5.9 °C) from the

control group ( QS), glazed with water ( Q0) and coated with 0.5% chitosan ( Q5) and 1.5%

chitosan ( Q15) after 6 months of storage at -22 °C; standard deviation corresponds to three

replications .............................................................................................................................. 57

Appendixes

Table A.1. Schematic representation of Standard Curve Determination for TBA method ................75

Table A.2. Scheme followed to obtain the calibration curves for K-value method ...........................79


1

Introduction

Nowadays, the changes in dietary patterns and the benefits credited to a healthy diet

revitalized intensively all sectors responsible for food production, leading to the search for alternative

processing, preservation and chemical alteration of these products (Fai et al., 2008). Marine foods

attract great attention from consumers as a source of important nutritional components for a

healthy diet (Simopoulos, 1997 cited in Rodriguez-Turienzo et al., 2011). Consequently, the

consumption of fish containing valuable nutrients has recently increased (Kilincceker et al., 2009).

In these contexts, the aim of this thesis and primary objective of this work focuses on assessing the

effect of applying an edible chitosan-based coating on the quality of frozen fish, in order to study the

possibility of using it as an alternative to water glaze, commonly applied in the fish industry. This

coating was applied on frozen Atlantic salmon (Salmo salar) directly, and its effect on the shelf life

of the fish was assessed, during frozen storage at -22 °C for 6 months.

With this in mind, this thesis was organized in two parts - Part I - State of The art and Part II

- Experimental work. Part I is composed of two chapters, Chapter 1 and Chapter 2 and Part II is

grouped in three chapters, Chapter 3, Chapter 4 and Chapter 5.

Chapter 1 provides an overview of the importance of fish in a healthy diet and the

increased of his demand. This chapter also reflects on the changes in the fish industry in

consequence of the increase of its consumption, while time providing an overview of this industry as

a major employer. This chapter also mentions the traditional methods of fish conservation, such as

freezing and glazing and other emerging methods, such as the edible coatings, in particular those

that are based on chitosan. The concept of quality and its relationship with freshness, in addition to

the different physical, chemical and biological processes that allows assessing it, are presented in

Chapter 2.

Chapter 3 introduces methods, such as sample preparation, the preparation of the coating

solutions, the transportation of samples and the determination of the values of TVC, TBA, TVB-N, K-

value, pH, and color parameters, regularly used in the evaluation and control of fish quality during

frozen storage. In Chapter 4, our results are discussed and in Chapter 5 the key findings of this

thesis are summarized, as well as suggestions for improvement and future prospects.


2


3

Part I – State of Art


4


5

Chapter 1. Fish

The demand for food that promotes health and well-being has increased in recent years.

The populations of many industrialized countries are becoming older, richer, more educated and

more health conscious. Fish has a particular prominence in this respect, following mounting

evidence confirming the health benefits of eating fish. More stringent demands to assurance food

safety are another high-profile issue that has emerged in recent years, in order to earn and maintain

consumer confidence in fish (FAO, 2012).

Consumers are increasingly requesting product attributes that depend on the production

process. They now demand guarantees that their food has been produced, handled and

commercialized in a way that is not dangerous to their health, respects the environment and

addresses various other ethical and social concerns. At the same time, they also want convenience

and palatability.

Besides trying to address consumer’s requests, producers and major distributors are

increasingly concerned about the sustainability, risk of depletion of marine stocks and the

transparency in traceability systems – in order to trace the source, the quality, and the

environmental and social impacts of food production and distribution (FAO, 2012).

Fuelled by changes in consumer taste and advances in technology, packaging, logistics and

transport, the food industry produces appealing and healthy fish products. These diversified

products include higher-value products, semi-processed and processed products, and products that

are ready to eat or require little preparation before serving. This is accomplished by the insertion of

improvements in storage and processing capacity, together with major innovations in refrigeration,

ice-making, and food-packaging and fish-processing equipment (FAO, 2012).


6

1.1. Fish Industry

The growth of the supply and consumption of fish

Capture fisheries and aquaculture supplied the world with about 148 million tonnes of fish

in 2010, of which about 128 million tonnes was utilized as food for people. From the fish meant for

direct human consumption, in 2010, 46.9% was live, fresh or chilled fish, followed by frozen fish

with 29.3%. Prepared or preserved fish and cured fish represented 14.0% and 9.8% respectively

from the total fish for human consumption (FAO, 2012).

The world food fish supply has grown in the last five decades, with an average growth rate

of 3.2 percent per year in the period 1961–2009, outpacing the increase of 1.7 percent per year in

the world’s population. Per capita, the world food fish supply increased from an average of 9.9 kg

(live weight equivalent) in the 1960s to 18.4 kg in 2009. There are large variations across countries

and regions of the world in the amount of total fish supply for human consumption, reflecting

different eating habits and traditions, availability of fish and other foods, prices, socio-economic

levels, and seasons. Differences are also evident within countries, with consumption usually higher

in coastal areas (FAO, 2012).

Fish industry as an employer

Fisheries and aquaculture also provided livelihoods and income for an estimated 54.8

million people engaged in the primary sector of fish production in 2010, of which an estimated 7

million were occasional fishers and fish farmers. In addition to the primary production sector,

fisheries and aquaculture provide numerous jobs in additional activities such as processing,

packaging, marketing and distribution, manufacturing of fish-processing equipment, net and gear

making, ice production and supply, boat construction and maintenance, research and

administration. All of this employment, together with dependants, is estimated to support the

livelihoods of 660 - 820 million people, or about 10 – 12% of the world’s population (FAO, 2012).


7

1.2. Fish - Chemical composition and structure

Due to its nutritional characteristics, fish is an important source of high grade protein and

the knowledge of its composition is essential if the fullest use is to be made of it (Murray & Burt,

2001). In matter of fact, chemical composition of fish is very important not only for the consumer,

but also for the processor, who needs to know the nature of the raw material before he can apply

correctly the techniques of chilling, freezing, smoking or canning; the nutritionist, who wants to

know what contribution fish can make to the diet and to health; and the cook, who must know the

fish in order to prepare it for the table (Murray & Burt, 2001).

However, chemical composition varies widely from fish to fish of the same species and also

within an individual fish, making accuracy impossible. Thus, measurement of constituents of fish

products is necessary to meet specifications or to comply with regulations (Murray & Burt, 2001).

The scheme in Figure 1.1 shows the part internal and the exterior surface of salmon. The

blocks of muscle are separated by thin sheets, which are known as connective tissue; these are

curved within the fillet and run from the backbone to the skin. In fresh fish the muscle blocks are

firmly attached to the connective tissue and the surface of a cut fillet is smooth and continuous.

There are also tiny blood vessels running through the muscle. The connective tissue accounts for

only a small percentage of the total weight of the muscle, making the fish less tough to eat than

meat (Murray & Burt, 2001).

Fish muscle (Figure 1.2) is of two kinds, light muscle and dark muscle. In white fish, such

as cod, there is a small strip of dark or red muscle just under the skin on both sides of the body,

running beneath the lateral line. In fatty fish, such as salmon, the strips of dark muscle are much

larger in proportion and contain higher concentrations of fat and certain vitamins. Since it is not

usually practicable to separate the dark fatty muscle from the light muscle when preparing fish for

cooking, as is made with the fat from meat, usually the values given in the tables for composition of

flesh are for the total muscle, taking light and dark together (Murray & Burt, 2001).

Components of fish muscle

The principal components of fish muscle, the edible part of the fish, are water, proteins and

fat. Other minor components are carbohydrates, minerals, vitamins and extractives (sugars, free

amino acids and nitrogenous bases) (Murray & Burt, 2001).


8

Source: adapted from http://www.greatcanadianrivers.com/salmon/species-biology-internal.html, consulted in 09/05/2013

Figure 1.1. Representative scheme of internal and external anatomy of salmon.

According to FAO (2012), fish and fishery products represent a valuable source of nutrients

of fundamental importance for diversified and healthy diets. Not only, fish is low in saturated fats,

carbohydrates and cholesterol, but also provides high-value protein, a wide range of essential

micronutrients, including various vitamins (D, A and B), minerals (including calcium, iodine, zinc,

iron and selenium) and polyunsaturated omega-3 fatty acids (docosahexaenoic acid and

eicosapentaenoic acid). Therefore, the consumption of fish, even in small amounts can have a

significant positive nutritional impact by providing essential amino acids, fats and micronutrients

that are scarce in vegetable-based diets. There is also evidence of beneficial effects of fish

consumption in relation to coronary heart disease, stroke, age-related macular degeneration, mental

health, and in terms of growth and development, in particular for women and children during

gestation and infancy for optimal brain development (FAO, 2012).

Water

The main constituent of fish flesh is water, which represents 30 to 90 per cent of the fillet

weight. Water, in fresh fish muscle, is tightly bound to proteins in the structure in such a way that it


9

cannot readily be expelled even under high pressure. After prolonged chilled or frozen storage,

however, proteins are less able to retain all the water, and some of it, containing dissolved

substances, is lost as drip. Frozen fish that are stored at too high a temperature, for example, will

produce a large amount of drip and consequently quality will suffer (Murray & Burt, 2001).

Source: adapted from http://www.earthlife.net/fish/muscles.html and http://www.fao.org/wairdocs/tan/x5916e/x5916e01.htm,

consulted in 09/05/2013

Figure 1.2. Diagram of fish muscle.

Proteins

The amount of protein in fish muscle is usually somewhere between 15% and 20%, but

values lower than 15% or as high as 28% are occasionally met with in some species. All proteins,

including those from fish, are chains of chemical units linked together to make one long molecule.

These units, of which there are about twenty types, are called amino acids, and certain of them are

essential in the human diet for the maintenance of good health. Two essential amino acids called

lysine and methionine are generally found in high concentrations in fish proteins. Fish protein

provides a good combination of amino acids that is highly suited to man’s nutritional requirements

(Murray & Burt, 2001).


10

Fat

The fat content can vary greatly among the various species of fish; even in the same specie

it can be considered a seasonal variation in fat content of fatty fish. The term fat is used for

simplicity, although the less familiar term "lipid" is more correct, since it includes fats, oils and

waxes, as well as more complex, naturally-occurring compounds of fatty acids. As the fat content

rises, so the water content falls, and vice versa; the sum of water and fat in a fatty fish is fairly

constant at about 80%. Although protein content falls very slightly when the fat content falls it

nevertheless remains fairly constant somewhere between 15% and 18%. The fat is not always

uniformly distributed throughout the flesh of a fatty fish. For example in Pacific salmon there may

be nearly twice as much fat in muscle from around the head as there is in the tail muscle (Murray

& Burt, 2001).

1.3. Fish Conservation

Fish is a very versatile food commodity, appearing in a great variety of ways and product

forms, such as live, fresh, chilled, frozen, heat-treated, fermented, dried, smoked, salted, pickled,

boiled, fried, freeze-dried, minced, filleted, powdered or canned, or as a combination of two or more

of these forms (FAO, 2012). While all of these methods of preserving fish at long-term are used, the

most important ones are those based on the action of low temperatures, because they are better

able to preserve the nutritional and sensory characteristics of the products (Gonçalves et al., 2009).

Fish processing is evolving, from simple gutting, heading or slicing to more advanced value-

addition, such as breading, cooking and individual quick-freezing, depending on the commodity and

market value (FAO, 2012).

1.3.1. Freezing

Freezing represents the main method of processing fish for human consumption and it is

the most used method in the control and/or reduction of biochemical changes that occur in fish

during storage. It accounted for 55.2% of total processed fish for human consumption and 25.3%

of total fish production in 2010. The proportion of frozen fish grew from 33.2% of total production

for human consumption in 1970 to reach a record high of 52.1% in 2010. Processors of traditional


11

products, in particular of canned products, have been losing market shares to suppliers of fresh and

frozen products as a result of long-term shifts in consumer preferences (Fan et al., 2009; FAO,

2012; Kilincceker et al., 2009; Rodriguez-Turienzo et al., 2011; Sathivel et al., 2007). However,

there are reports of progressive loss of intrinsic and sensory quality of frozen fish during storage

(Vanhaecke et al., 2010). In fact, if on one hand, the use of temperatures below -12 °C inhibit

microbial growth and slows down enzymatic activity (Jiang & Lee, 2004 cited in Rodriguez-Turienzo

et al., 2011), on the other hand, freezing is not able to completely inhibit microbial and chemical

reactions, such as lipid oxidation, protein denaturation and dehydration surface (sublimation and

recrystallization of ice crystals) leading to deterioration of fish quality during prolonged storage,

resulting in off-flavors, rancidity, dehydration, weight loss, loss of juiciness, drip loss and

toughening, as well as microbial spoilage and autolysis (Fan et al., 2009; Gonçalves et al., 2009;

Rodriguez-Turienzo et al., 2011 Sathivel et al., 2007).

Although, despite the occurrence of a certain deterioration of the quality of frozen foods

during storage, freezing increases the shelf life of the products if carried out correctly. Thus, the

extent of loss quality depends on many factors including the rate of freezing and thawing, storage

temperature, temperature fluctuations, abuse of freeze-thaw during storage, transport, exposure and

consumption. It should be noted that freezing does not improve product quality; the final quality

depends of the quality of the product in the moment of freezing and the conditions during freezing,

storage and distribution (Gonçalves et al., 2009).

Effect of freezing rate on ice crystal structure

Hayes et al. (1984) cited in Evans (2008) define the freezing rate in relation to the velocity

of movement of the ice-water freezing front. The rates of freezing determine the type, size and

distribution of ice formation.

Evans (2008) points out that freezing removes water form the food matrix by forming ice

crystals. Although ice crystals remain in the food, the remaining water that is in contact with the

food matrix becomes concentrated with solutes and it’s aw becomes low. Foods with a lower water

activity are more stable, since most microorganisms cease functioning below the water activity of

about 0.7. However, the formation of ice crystals can downgrade the quality of the food by three

mechanisms: mechanical damage to the food structure, cross-linking of proteins and limited re-

absorption of water on thawing (drip loss).


12

According to Fellows (2000), different tissues have different resistances to freezing damage.

Products with a more flexible fibrous structure which separates during freezing instead of breaking,

are not seriously damage their texture, while products that have a more rigid cell structure may be

damaged by ice crystals. The extent of damage depends on the size of the crystals and hence on

the rate of heat transfer. During slow freezing, ice crystals grow in intercellular spaces and deform

and rupture adjacent cell walls. Cells become dehydrated and permanently damaged by the

growing crystals. On thawing, cells do not regain their original shape and turgidity. The food is

softened and cellular material leaks out from ruptured cells, increasing the drip loss. In fast freezing,

smaller ice crystals form within both cells and intercellular spaces. There is little physical damage to

cells, and minimal dehydration of the cells. The texture of the food is thus retained to a greater

extent. However, very high freezing rates may cause stresses within some foods that result in

splitting or cracking of the tissues. Zhu et al. (2004) confirms this by saying that freezing process

was generally much more important than thawing for drip loss, once slower freezing produces larger

extra-cellular ice crystals, resulting in more tissue damage and thawing loss (Fennema, 1973 cited

in Zhu et al., 2004).

Another process which causes the same result is migratory recrystallisation, largely caused

by fluctuations in the storage temperature. This process causes the melting of ice crystals and

movement of moisture to regions of lower vapor pressure, which leads areas of the food nearest to

the source of heat to become dehydrated (freezer burn). When the temperature falls again, the

existing ice crystals increase their size. There is therefore a gradual reduction in the numbers of

small crystals and an increase in the size of larger crystals, resulting in loss of quality similar to that

observed in slow freezing. This is minimized by packaging in moisture-proof materials (Fellows,

2000). For this reason, as soon as seafood is removed from a freezer, they should be glazed or

wrapped (unless they have been packaged before freezing) and immediately transferred to a low

temperature store to rapidly refreeze and to preserve taste, smell and texture as well as to minimize

thaw drip loss (Gonçalves et al., 2009; Jose & Sherief, 1993 cited in Jacobsen & Fossan, 2001).

1.3.2. Glazing

The application of a thin layer of ice on the surface of the frozen products by spraying or

dipping into a water bath, it is common practice in frozen fish industry, in a process termed glazing

(Gonçalves et al., 2009; Vanhaecke et al., 2010). This technique aims at minimizing the impact of


13

undesirable changes on the quality of frozen products during storage (Gonçalves et al., 2009;

Vanhaecke et al., 2010). This water glaze excludes air from the surface of the product, thus

reducing the rate of oxidation and also serves as a protective barrier to temperature fluctuations,

allowing the glaze evaporate instead tissue water, when an increase of temperature occurs (Fossan

& Jacobsen, 2001 cited in Gonçalves et al., 2009). It is intended that the entire surface of the

product be completely and uniformly glazed, typically with a percentage of glazing applied between

4% and 10%, although it may vary between 2% and 20% (Vanhaecke et al., 2010). This percentage

depends on the immersion time, the temperature of fish and water and the size and shape of the

fish (Fossan & Jacobsen, 2001; Johnston et al., 1994; Pedersen & Jacobsen, 1997 cited in

Gonçalves et al., 2009). Thus, for a glazing less than 6%, a deficiency can occur in product

protection and for a glazing greater than 12%, commercial disputes can be generated because

excess water would entail additional costs for consumers (Vanhaecke et al., 2010).

According to DL nº 37/2004, in an attempt to protect the consumers interests was

adopted an official method for sampling and determination of drained net weight, which gave rise to

NP 4355:2002. It has also become mandatory to show in the foodstuffs labels, information about

your drained net weight and its price, which allowed consumers to know the amount of water that is

being sold with the product.

1.3.3. Edible coatings/films

New technologies are being use to ensure the conformity of frozen fish during storage trying

to satisfy the growing demand for this product. In some experiments, natural products were used to

ensure the quality of the fish and the extension of its validity (Sathivel et al., 2007; Souza et al.,

2010).

Edible films and coatings have become one of the most promising alternatives to protect

the products against mechanical damage, physical, chemical and microbiological activities. These

are thin layers of edible material that, when applied in food, assist in their preservation, distribution

and marketing (Falguera et al., 2011; Pinheiro et al., 2010).

The application of edible coating in foods has been evaluated by several authors as: Ribeiro

et al. (2007) on the surface of strawberries; Cerqueira et al. (2009b) in tropical fruits such as

acerola (Malpighia emarginata), cajá (Spondias lutea), mango (Mangifera indica), pitanga (Eugenia

uniflora) and seriguela (Spondias purpurea); Cerqueira et al. (2009a) on the surface of cheese;


14

Martins et al. (2010) in Ricotta cheese and Souza et al. (2010) in mango "Tommy Atkins" (cited in

Pinheiro et al., 2010). Other examples can be found regarding the development of coatings to

protect food products like garlic (Botrel et al., 2007), India cherries (Ziziphus mauritina cv. Cuimi)

(Qiuping & Wenshui, 2007) and red pitaya (Hylocereus undatus) (Chien et al., 2007) (cited in Fai et

al., 2008).

These coatings/films have particular properties that make them particularly useful for

application in perishable products. Some of those properties are structural resistance to water and

microorganisms, functional attributes (antibiotics, antifungal, antibacterial, etc.), mechanical

properties (tension and flexibility), optical properties (brightness and opacity), the barrier effect

against gases flow and high acceptability (Falguera et al., 2011).

The main difference between films and coatings is how they are applied in food. While the

coatings are applied by immersion of the product in a solution, the films are first shaped as solid

sheets, like a package, and then applied to the product (Falguera et al., 2011; Kilincceker et al.,

2009).

Coatings/films can be produced by a variety of biodegradable polymers such as

polysaccharides, proteins, lipids, resins, with or without the addition of plasticizers and surfactants.

The functionality and behavior of edible films and coatings depend mainly on their mechanical and

transport properties, which in turn vary with their composition, formation process, and the method

of application in the product (Pinheiro et al., 2010; Rodriguez-Turienzo et al., 2011).

These coatings/films create a modified atmosphere that restricts the transfer of gases such

as O2, CO2 and aromatic compounds, influencing several parameters in fresh and minimally

processed food, such as color, texture, sensory quality, antioxidant properties, production of

ethylene volatile compounds and microbial growth as a result of anaerobic processes (Falguera et

al., 2011; Pinheiro et al., 2010; Rodriguez-Turienzo et al., 2011). Developed to reduce, inhibit or

prevent the growth of microorganisms on food surfaces where microbial contamination is more

significant, their application innovated the concept of active packaging (Falguera et al., 2011;

Pereira et al., 2010).

1.3.3.1. Chitosan

Chitosan (Figure 1.3 (b)) is a natural amino-cationic hetero-polymer composed of ß-1.4 D-

glucosamine units, linked to N-acetylglucosamine residues, which can be obtained by chitin


15

deacetylation. Chitin (Figure 1.3 (a)) is an abundant natural polymer, a linear polysaccharide alkali-

acid insoluble, which has ß-1,4 N-acetylglucosamine as monomeric unit. It can be found in the

exoskeleton of crustaceans, insects and fungal cell walls (Fai et al., 2008).

The use of chitin in many industrial processes generates a large amount of solid waste. The

valorization of these residues through their transformation into other products, such as chitosan, is

a way to reduce wastage (Pinheiro et al., 2010).

Chitosan attracts much attention in the food industry due to its viscoelastic properties and its

particular properties such as non-toxicity, bioactivity, biodegradability, biocompatibility, reactivity,

selective permeability, polyelectrolytic action, the ability to form gels and films, the adsorption

capacity, the ability antibacterial, antifungal, antimicrobial and antioxidant. Thus, the chitosan-

based materials can be used for producing edible films and coatings resistant, durable and flexible,

with mechanical properties comparable to commercial polymers (Fan et al., 2009; Pinheiro et al.,

2010; Sathivel et al., 2007).

Source: removed from Fai et al. (2008)

Figure 1.3. Chemical structure of chitin (a) and chitosan (b).


16


17

Chapter 2. Fish Quality - Freshness

Monitoring and control quality of frozen fish is one of the fundamental purposes of the

seafood industry. Many parameters are involved in the definition of quality (Figure 2.1), including

safety, nutritional and sensory properties, price, convenience and constancy, color packaging,

availability and freshness (Ólafsdóttir et al., 1997; Souza et al., 2010). In fact, the maintenance of

quality parameters, by developing effective techniques on their conservation, it is essential to make

food more appealing to the end consumers (Gonçalves et al., 2009; Pinheiro et al., 2010). The

change of one of these parameters affects largely the product acceptability by the consumers and

consequently also the commercial value (Rodriguez-Turienzo et al., 2011).

Freshness is one of the most important parameters for the quality of the final product. In

fact, quality is a function of freshness, although this is not a priori a factor sufficient to guarantee it.

Freshness can be translated by some sensory, (bio)chemical, physical and microbiological

parameters determined by different analyzes, which extend from the time of harvesting to product

deterioration. These tests claim to detect variations in flavor, texture, color, odor, and other

parameters that affect the freshness and alter perception and consumer satisfaction (Kilincceker et

al., 2009; Ólafsdóttir et al., 1997).

2.1. Microbiology

2.1.1. Total Viable Counts (TVC)

The microbial activity is the main factor limiting the shelf life of fresh fish, and the main

cause of its deterioration. The total viable count (TVC) is used as an acceptability index in

standards, guidelines and specifications, since this method can provide a useful measure of the

freshness of fish (Ólafsdóttir et al., 1997). According to ICMSF (1986), an aerobic plate count (APC)

is recommended for all products as a good indicator from storage length and conditions of products

prior to stabilizing processes such as freezing. Thus APC is indicative of general quality and, to a

lesser extent, of handling and storage procedures.

Newly caught fish contain a diverse microflora, usually around 102 -106 CFU/g on whole fish

and cut fillets. Usually, in fish products, TVC around 107-108 CFU/g, lies at the point of sensory

rejection. Nevertheless, standards, guidelines and specifications often use much lower TVC as


18

indices of acceptability. Microbial criteria based on low TVC, such as 106 CFU/g, are problematic to

use, because a correlation between TVC and the remaining shelf life is assumed, although generally

not known (Ólafsdóttir et al., 1997). In matter of fact, an increase in APC to levels in excess of 106

per gram is usually indicative of long storage at chilling temperatures or temperature abuse prior to

freezing (ICMSF, 1986).

In order to properly evaluate fish freshness, microbial methods are developed together with

mathematical models, which express the effects of storage conditions - such as temperature and

atmosphere - on the correlation between microbial numbers and remaining shelf life. The most

promising results were obtained with relatively slow detection methods such as plate counts and

other growth techniques involving an incubation period (Ólafsdóttir et al., 1997).

Source: removed from Ólafsdóttir et al. (1997)

Figure 2.1. Relationship between quality and freshness.


19

Psychrophilic and psychrotrophic microorganisms

Microorganisms can be classified according to their growth rate at different temperatures.

According to this classification, microorganisms that are expected to grow at freezing temperatures

will be psychrophiles and psychrotrophs (Figure 2.2).

Source: removed from http://classroom.sdmesa.edu/eschmid/Lecture4-Microbio.htm, consulted in 14/06/2013

Figure 2.2. Temperature ranges for different microbial life forms.

Much of life on Earth has evolved to colonize low-temperature environments. In fact, the

cold biosphere (temperatures permanently below 5 °C) represents by far the largest fraction of the

global biosphere (Casanueva et al., 2010; Cavicchioli, 2006; Feller & Gerday 2003; Margesin &

Miteva, 2011; Siddiqui & Cavicchioli, 2006 cited in Siddiqui et al., 2013). Actually, the lowest

temperature limit for life seems to be around -20 °C, which is the value reported for bacteria living

in permafrost soil and in sea ice. Microbial activity at such temperatures is restricted to small

amounts of unfrozen water inside the permafrost soil or the ice, and to brine channels. Aerobic and

anaerobic bacteria are found at these temperatures (D’Amico et al., 2006).

Psychrophilic microorganisms have successfully colonized all permanently cold

environments from the deep sea to mountain and Polar Regions. Some of these organisms,

depending on their optimal growth temperature, are also known by the terms psychrotolerant or

psychrotroph, but the general term used to designate all microorganisms growing well at


20

temperatures around the freezing point of water is psychrophiles. The ability of psychrophiles to

survive and proliferate at low temperatures implies that they have overcome key barriers inherent to

permanently cold environments, such as reduced enzyme activity; decreased membrane fluidity;

altered transport of nutrients and waste products; decreased rates of transcription, translation and

cell division; protein cold-denaturation; inappropriate protein folding; and intracellular ice formation

(D’Amico et al., 2006).

2.2. Lipids

2.2.1. 2-Thiobarbituric acid (TBA)

For measuring lipid oxidation in foods, different analytical methods are used. It is necessary

to select a proper and adequate method for a particular application. Five groups divide the available

methods to monitor lipid oxidation in foods, based on what they measure: the absorption of oxygen,

the loss of initial substrates, the formation of free radicals, and the formation of primary and

secondary oxidation products. A number of physical and chemical tests, including instrumental

analyses, have been employed in laboratories and the industry for measurement of various lipid

oxidation parameters. These include the weight-gain and headspace oxygen uptake method for

oxygen absorption; iodometric titration, ferric ion complexes, and Fourier transform infrared (FTIR)

method for peroxide value; chromatographic analysis for changes in reactants; spectrometry for

conjugated dienes and trienes, 2-thiobarbituric acid (TBA) value, p-anisidine value (p-AnV), and

carbonyl value; Rancimat and Oxidative Stability Instrument (OSI) method for oil stability index; and

electron spin resonance (ESR) spectrometric assay for free-radical type and concentration;

differential scanning calorimetry (DSC) and nuclear magnetic resonance (NMR). In addition,

sensory tests provide subjective or objective evaluation of oxidative deterioration, depending on

certain details (Shahidi & Zhong, 2005).

Due to its lipid composition, fish is highly susceptible to oxidation, which translates into

changes in odor, flavor, texture, color and nutritional value. Oxidation becomes an important factor

of deterioration, particularly at temperatures below 0 °C (Ólafsdóttir et al., 1997), and results from

the instability of primary oxidation products, such as hydroperoxides, which decompose into

secondary oxidation products such as aldehydes, ketones, alcohols, hydrocarbons, volatile organic


21

acids and epoxy compounds, among others (Figure 2.3) (Mendes et al., 2009; Shahidi & Zhong,

2005).

Source: removed from Huss, 1995 available in

http://www.fao.org/docrep/v7180e/V7180e06.htm#5.4%20Lipid%20oxidation%20and%20hydrolysis, consulted in 14/06/2013

Figure 2.3. Autoxidation of polyunsaturated lipid.

Malondialdehyde (MDA), one of the most important products of oxidation, is often used as

marker of oxidative damage in biological samples and in food. A simple method for the

determination of MDA is the spectrophotometric detection of thiobarbituric acid-malondialdehyde

complex (TBA-MDA) obtained after reaction with 2-thiobarbituric acid (TBA) at low pH and high

temperature (Figure 2.4) (Mendes et al., 2009).

Source: adapted from Bastos et al. (2012)

Figure 2.4. TBA test reaction between 2-thiobarbituric acid and malonaldehyde, forming a colored compound, measured in a spectrophotometer at 530 nm.

Although the TBA test is frequently used to assess the oxidative state of a variety of food

systems, it has limitations, such as lack of specificity and sensitivity, since many other substances

may react with the TBA reagent and contribute to absorption, causing an overestimation of the


22

intensity of color complex. These interferences may come from additional absorption of other

alkanals, 2-alkenals, 2.4-alkdienals, ketones, ketosteroids, acids, esters, proteins, sucrose, urea,

pyridines, and pyrimidines, also referred to as TBARS (Shahidi & Zhong, 2005).

2.3. Volatiles

2.3.1. Total Volatile Basic Nitrogen (TVB-N)

Odor is one of the most important parameters used to evaluate fish freshness, which can

be monitored by measurement of characteristic volatile compounds. Several of these compounds

can be used to monitor the freshness or spoilage stage of fish and to assess its quality, including

the value of total volatile nitrogen (TVB-N). These volatile compounds can be divided into three

groups based on their origin, as illustrated in Figure 2.5 (Ólafsdóttir et al., 1997). Extractive

compounds, particularly the volatiles, whose concentration in fish varies directly with time of

storage, have long been studied since they may provide indicators of the quality of fish. When fish is

stored after capture, the amount of some of the extractives present will change with time; thus

measurement of the amount can often indicate the storage time and hence indirectly the quality

(Murray & Burt, 2001).

Source: removed from Ólafsdóttir et al. (1997)

Figure 2.5. Categorization of fish odours and the volatile compounds that contribute to the characteristic odour of fresh, spoiled and oxidized fish.


23

The TVB-N limit established for the various fish categories is:

25 mg nitrogen/100 g

Sebastes ssp.

Helicoelenus dactylopterus

Sebastichtys capensis


species belonging to the family pleuronecitidae (with the exception of halibut) -

Hippoglossus sp.


Salmo Salar

species belonging to the family Merlucciidae

species belonging to the family Gadidae

Routine methods used to control the threshold TVB-N are the following:

microdiffusion method described by Conway and Byrne (1933)

direct distillation method described by Antonacopoulos (1968)

distillation of an extract deproteinized with trichloroacetic acid [Codex Alimentarius

Committee on Fish and fishery products (1968)] (Directive 95/149/EC).

2.4. Adenosine-5’-triphosphate (ATP)

2.4.1. K-value

Oxidation starts immediately after catch, but becomes particularly important for shelf life

only at temperatures below 0 °C, when oxidation rather than microbial activity becomes the major

spoilage factor. The initiation of lipid oxidation arises from various early post mortem changes in fish

tissues (Ólafsdóttir et al., 1997).

Rigor mortis is one of the most prominent changes in muscle occurring soon after death.

When fish are killed while relaxed, creatine phosphate is degraded prior to the breakdown of

adenosine triphosphate (ATP) (Figure 2.6). When the creatine phosphate and ATP reach about the

same concentration as ATP, ATP content begins to decrease and rigor mortis starts. Rigor mortis

occurs when crossbridge cycling between myosin and actin in myofibrils ceases, and permanent

actin and myosin linkages are formed. However, rigor mortis is resolved after some time. Possible


24

causes of post mortem tenderization include a weakening of Z-discs of myofibrils, a degradation of

connective tissue or a weakening of myosin-actin junctions (Wang et al., 1998). In post mortem fish

muscle degradation of adenosine triphosphate (ATP) proceeds according to the sequence

(Ólafsdóttir et al., 1997; Özogul et al., 1999; Souza et al., 2010):

ATP (adenosine thriphosphate) → ADP (adenosine diphosphate) → AMP (adenosine monophosphate) →

IMP (inosine monophosphate) → HxR (inosine) → Hx (hypoxanthine)

Following death, ATP is rapidly degraded to IMP by endogenous enzymes (autolysis). The

further degradation of IMP to inosine and hypoxanthine is much slower, and is catalyzed mainly by

endogenous IMP phosphohydrolase and inosine ribohydrolase, with a contribution from bacterial

enzymes as storage time increases. Therefore, the degradation of ATP is parallel to loss of freshness

of the fish. Thus, a chemical index of fish freshness is appealing because it is quantifiable, objective

and lends itself to automation. ATP alone cannot be used because it is so rapidly converted to IMP

and the concentrations of its intermediate degradation products rise and fall, making them

unreliable indexes of freshness. As a result, attention has focused on inosine and hypoxanthine, the

terminal catabolites of ATP. Inosine accumulates in some species of fish whereas hypoxanthine

accumulates in others as terminal catabolites (Ólafsdóttir et al., 1997). The K-value is used as an

index for the estimation of fish freshness and it is defined as the ratio of the sum of inosine and

hypoxanthine concentrations to the total concentration of ATP metabolites (Lin, 1993; Ólafsdóttir et

al., 1997; Souza et al., 2010). So, a fresh fish will have a low K-value. A shortcoming of the K-value

as a freshness index is its dependence on a variety of variables. It varies between species owing to

differences in rates of ATP degradation. It also varies with post mortem time and temperature

storage conditions, handling conditions and method of kill. Thus, a profile of K-value versus time

must be established for each species and its specific handling and storage conditions before K-

value measurements can be used to evaluate freshness. Following acid extraction and

neutralization, metabolites are separated by ion-exchange chromatography or HPLC and quantified

by their absorbance. Although other methods have been used, HPLC method is the most reliable

(Ólafsdóttir et al., 1997).


25

Source: removed from Huss, 1995 available in

http://www.fao.org/docrep/v7180e/V7180e06.htm#5.4%20Lipid%20oxidation%20and%20hydrolysis, consulted in 14/06/2013)

Figure 2.6. Aerobic and anaerobic breakdown of glycogen in fish muscle.

2.5. pH

High water-holding capacity, neutral pH values, enzymes contained in the tissues, and

lower connective tissue content have acceleratory effects on the process of spoiling (Kilincceker et

al., 2009). According to Huss (1995), the knowledge about the pH of fish meat may give valuable

information about its condition. At the moment of fish death, the normal respiration process cannot

occur, the oxygen supply is interrupted, the blood circulation fails and the production of energy is

limited. So, in the beginning, because of rigor mortis, the maximum level of lactic acid is present in

the structure, decreasing the pH. Although, the pH of the fish fillet increases, in the post mortem

period, in consequence of the decomposition of nitrogenous compounds, by the proteolytic bacteria

and autolytic enzymes. This increase in pH has a negative effect on the quality of the product

during storage; especially, the sensorial characteristics such as odor, color, and texture (Shenderyuk

& Bykowski, 1989 cited in Kilincceker et al., 2009).

Fan et al. (2009) confirms this trend claiming the pH value decreases initially and then

increases, indicating similar observations by Alasalvar et al. (2001) and Manju et al. (2007). The

initial pH decrease may be attributed to the dissolution of CO2 in the fish sample, while the increase

of pH was postulated to be due to an increase in volatile bases produced, e.g. ammonia and

trimethylamine (TMA), by either endogenous or microbial enzymes (Manat et al.,2005; Ruiz-

Capillas & Moral, 2001 cited in Fan et al.,2009).


26

According to Sigurgisladottir et al. (2000) cited in Rodriguez-Turienzo et al. (2011), freezing

cause changes in pH values of fish muscle; this is probably due to the increase in concentration of

substances in the water that remains unfrozen in frozen foods and modifies the acid–base

equilibrium; there is a tendency towards higher acidity (Jiang & Lee, 2004 cited in Rodriguez-

Turienzo et al., 2011).

2.6. Sensory Analysis

2.6.1. Color

Color is all around us. Although, an infinite number of colors surround us, nothing really

has color. Color is a perception and subjective interpretation, generated in the eye-brain system in

response to given stimuli. Color communicates and sells, driving the sale of virtually every

consumer product in the world. It evokes a wide range of emotions that draw the buyer to the

product (Hewlett-Packard Development Company, 2008; Konica Minolta, 2003; X-Rite, 2004).

Color, as one aspect of appearance, is one of the major attributes that affect the consumer

perception of quality. It has to be within an expected range for food acceptance, and the degree of

acceptability is judged within that range. Nearly every food product has an acceptable range of

color, depending on a wide range of factors, like ethnic origin of the consumer and of the food, age

and sex of the consumer, physical surroundings at the time of viewing, health consciousness of the

consumer, physical well-being, among others (Francis, 1995; HunterLab, 2008). This makes color

an important marketing communication tool and a crucial part of the selling process, interfering in

buyer decision (Garber et al., 2000). So, measurement is the key to total production control,

allowing the establishment of precise measurement standards that can be repeated in the process

production of identical items within quality tolerances (X-Rite, 2004). To use color effectively, it must

be kept under control, answering the customer’s specifications. The best way to control co lor is by

measuring it, because if you can measure color, you can control it.

Light, object and viewer are three essential elements to perceive color, since it results from

an interaction between them. In matter of fact, in total darkness and if we close our eyes, we

cannot see anything, not even colors. And if there is only an object, the color simply does not exist.

All three elements must be present for the color as we know it exists. For the viewer to perceive the

light as a distinct color, it must be modified by the object (Konica Minolta, 2003; X-Rite, 2004).


27

Light

The light is just one of several electromagnetic waves that exist in space and which together

constitute the electromagnetic spectrum (Figure 2.7). The region of visible light is only a small part

of the spectrum located between about 400-700 nm. The light reflected from an object, which we

recognize as being his color, is the mixture of light at various wavelengths, within the visible region.

The human eye can see light in the visible region of the electromagnetic spectrum, however, "light"

is not the same as "color". The light is radiation that stimulating the retina of the eye, making it

vision possible. The stimulation of the eye is transmitted to the brain, and it is here that the concept

of "color" first occurs as the brain's response to information received from the eye. These stimuli

are perceived by the brain as a particular color. Exactly which color is perceived depends on the

composition of wavelengths in the light waves. However, our vision system responds to each

individual wavelength. For instance, in passing a beam of white light through a prism, the light is

dispersed and different colors are seen as respond of the eyes to each individual wavelength. So,

different wavelengths cause us to see different colors. On the other hand, we rarely see all

wavelengths at once (pure white light), or just one wavelength at once. Color appears when light is

modified into a new composition of many wavelengths by interaction with an object. This is how all

objects get their color - by modifying light, which is send to our eyes as a unique composition of

wavelengths (HunterLab, 2008; Konica Minolta, 2003; X-Rite, 2004).

Object

Every object absorbs and reflects light spectrum into portions and different amounts. When

light waves strike an object, the object’s surface absorbs some of the spectrum’s energy, while

other parts of the spectrum are reflected back from the object. The modified light that is reflected

from the object has an entirely new composition of wavelengths. Light can be modified by striking a

reflective object such as paper; or by passing through a transmissive object such as film. Reflected,

transmitted, or emitted light is, in the purest of terms, “the color of the object” (Figure 2.8). There

are as many different colors as there are different object surfaces, because each object affects light

in its own unique way. The color of the object varies with the viewing conditions, viewing angle and

angle measurement (Konica Minolta, 2003; X-Rite, 2004).


28

Source: adapted from X-Rite (2004)

Figure 2.7. Electromagnetic and visible spectra.

Source: removed from X-Rite (2004)

Figure 2.8. Different objects surfaces modifying the light.


29

Viewer

Without the viewer, there would be no sensory response that would recognize or register the

wavelengths as a unique “color.” If an object is not seen, he doesn’t have color. Technically, color

is there in the form of wavelengths. So, the concept of color only happens in our minds, after our

visual sensory system has responded to those wavelengths. The basis for human vision is the

network of light sensors in our eyes. These sensors respond to different wavelengths by sending

unique patterns of electrical signals to the brain. In the brain, these signals are processed into the

sensation of sight - of light and of color. Our memory system recognizes distinct colors; this system

breaks the visible spectrum into its most dominant regions of red, green and blue, and then

concentrates on these colors to calculate color information (Figure 2.9) (Konica Minolta, 2003; X-

Rite, 2004).


Figure 2.9. RGB system – Color’s Additive Primaries.

Dimensions of color

When one says ‘measure the color’ what it means is ‘locate the color’ in terms of

coordinates in a three-dimensional color solid (Figure 2.10) (Francis, 1995). Color has three

attributes - hue (basic color), saturation (vividness or dullness) and lightness (brightness or

darkness) - which can be arranged together to create a three-dimensional solid. The wavelength

determines the color’s hue; wave purity determines saturation; and wave amplitude (height)

determines lightness. Hue, saturation, and lightness demonstrate that visible color is three-

dimensional. These attributes provide three coordinates that can be used to “map” visible color in a

color space. There are many different types of color spaces and they can be used to describe the

range of visible or reproducible colors of a viewer or device. This three-dimensional format is also a

very convenient way to compare the relationship between two or more colors (Konica Minolta,

2003; X-Rite, 2004).


30

Source: adapted from X-Rite (2004) and http://www.paintbasket.com/members/index.php?topic=121.0, consulted in 29/08/2013

Figure 2.10. Munsell Color System (HSL – the three dimensions of color).

Color space (CIE L*a*b*)

The color space CIE L*a*b* is uniform color space, which uses a repeatable system of

color communication standards. These standards’ most important function was to provide a

universal framework for color matching. The L*a*b* color model uses rectangular coordinates

based on the perpendicular yellow-blue and green-red axes. One of the advantages of spectral data

is its ability to predict the effects of different light sources on an object’s appearance. This color

space has been defined by the CIE in 1976 to reduce one of the biggest problems of space Yxy

original: that equal distances on the chromaticity diagram x,y do not correspond to an equal

perception of color differences. In this improved space, equal distances in the coordinates of the

diagram correspond to equally perceived color differences. Thus, L* indicates lightness and a* and

b* are the chromaticity coordinates. In the a*,b* chromaticity diagram, a* and b* indicate color

directions: +a* is the red direction, -a* is the green direction, +b* is the yellow direction and -b* is

the blue direction (Figure 2.11). The center is achromatic. With the increase of the values of a* and

b* and the point moves away from the center, the color saturation increases (Konica Minolta, 2003;

X-Rite, 2004).


31


Figure 2.11. Color space (CIE L*a*b*) – Mapping Color’s dimensions.

The colorimeter

Unlike the human eye, a colorimeter can measure a color accurately and easily. As

previously noted, unlike subjective expressions commonly used by people to describe the colors

verbally, the colorimeters express colors numerically according to international standards. In

addition, the personal perception of a particular color may vary depending on the background or the

light source used. The colorimeters correspond to the functions of the human eye, but as they

always make measurements using the same light source and the same lighting method, the

measurement conditions are always the same, day, night, indoor or outdoor environments (X-Rite®,

2004). In the color space L*a*b* color difference can be expressed by a single value, ∆E*ab, which

indicates the size of the color difference, but doesn’t shows how the colors are different. This is,

∆E*ab indicates the degree of color difference, but not the direction. ∆E*ab is defined by the

following Equation 1:

Equation 1

Where ∆L*, ∆a*, ∆b* indicate the difference in values L*, a* and b* found between the sample

color and the color of the standard (Konica Minolta, 2003; X-Rite, 2004).

Black

Yellow

Green

White

Blue

Red


32

CIE L*a* b* Tolerance Method

Generally, the color of a product may be judge to be “acceptable” or “unsatisfactory”. Such

judgments can be visually or instrumentally based on a perceived difference between an ideal

product standard and a sample. When this difference is quantified, tolerances can be established.

Tolerances are limits within a product are considered acceptable. Any product falling outside the

tolerances is unacceptable. There are two levels of visual color differences that are used to establish

color tolerances (Figure 2.12):

Minimum perceptible difference, which defines a just-noticeable difference between

standard and sample;

Maximum acceptable difference, which is the largest acceptable difference between

standard and sample.

Manufactures are generally concerned about the maximum acceptable color difference rather than

minimum perceptible difference, and color tolerances are usually based on it. Any difference than

that would cause the sample to be rejected (HunterLab, 2008).

Source: removed from HunterLab (2008)

Figure 2.12. Illustrative scheme of perceptible versus acceptable differences.

Using CIE L*a*b*, the standard color - or original specification - is pinpointed by its

measurement data in the L*a*b* color space. Then, a theoretical “tolerance sphere” is plotted

around the color. This sphere represents the acceptable amount of difference between the standard


33

and other measured samples (the color output). Data that falls within the tolerance sphere

represents an acceptable color. Measurements that fall outside the tolerance sphere are

unacceptable (X-Rite, 2004).


Figure 2.13. Tolerance sphere for acceptable color difference.

The size of the tolerance sphere (Figure 2.13) is determined by customer’s specifications

for acceptable color difference, which is expressed in delta (∆) units such as ∆E (delta error) (X-

Rite, 2004). According to X-Rite (2004), typical customer tolerance in the graphic arts industry

usually lies between 2 and 6 ∆E. Differences between colors in an image that are within 4∆ units

of each other often are not visible to most viewers. According to Ahmad (2006), a minor difference

in color perceived by the human eye is in the range of ∆E*ab 0.3 to 0.5. However, the acceptable

thresholds are much higher, standing at intervals of 1.1 to 2.1.

2.7. Physical properties

2.7.1. Temperature and storage time

According to Evans (2008), the shelf-life of a frozen food is a complex concept that

depends on the characteristics of the food product and the environmental conditions to which the

food is exposed after being subjected to the freezing process. The International Institute of

Refrigeration (IIR, 1986) has recommended two definitions: practical storage life (PSL) and high-

quality life (HQL). The PSL (or acceptability time) is “the period of proper frozen storage after

freezing of an initial high-quality product, through which the frozen food retains its quality


34

characteristics and is suitable for consumption or for use in further process”. HQL parameter (or

Just Noticeable Difference (JND)) is defined as “the storage period through which the initial quality

was maintained from the time of freezing up to the point where 70% of the trained taste panel

members are capable of detecting a noticeable difference between the frozen food stored at

different temperatures and the corresponding controls stored at -40 °C in a triangular sensory test”

(Evans, 2008). However, these concepts diverge from the concept of storage life that is acceptable

to consumers, which may be acceptable for three to six times longer than the PSL or HQL. These

methods therefore measure the period that food remains essentially the same as when it was

frozen. Fluctuating temperatures and the type of packaging used are the main causes of loss of

storage life. Since temperature fluctuation has a cumulative effect on food quality, the proportion of

PSL or HQL lost can be found by integrating losses over time. Time temperature tolerance (TTT)

and product processing packaging (PPP) concepts are used to monitor and control the effects of

temperature fluctuations on frozen food quality during production, distribution and storage (Fellows,

2000).

Knowledge of the various changes that occurs in fish, immediately after harvest or catch,

must be known, especially the changes in properties that take place over time. This information can

be gained by performing controlled storage experiments that extend from the time of harvest until

spoilage.

Freshness, loss of freshness and spoilage can also be monitored using different techniques.

However, only a few of these techniques are routinely applied in the fish industry, because they are

time-consuming, require expensive laboratory equipment and trained personnel (Ólafsdóttir et al.,

1997). In matter of fact, freezing and frozen storage can give a shelf-life of more than one year if

properly carried out (Chevalier et al., 2001; Johnston et al., 1994; Regenstein & Regenstein, 1991

cited in Gonçalves et al., 2009).

According to Reid et al. (2003), shelf-life estimation for frozen foods can be a long process

because of the long duration of shelf-life at the lower temperatures of storage. The purpose of low-

temperature storage is to achieve extended shelf-lives. A reliable procedure that allows an effective

estimation of the low-temperature storage life of a product, utilizing data collected on the product in

question, requires around 60 days, while effectively estimating the storage temperatures required to

achieve target shelf-lives of 1 year, 18 months, 2 years or even longer.

There is a need for procedures that will allow for the estimation of extended shelf-lives,

especially at low temperatures, without requiring too long an evaluation period. Many procedures


35

exist for accelerated shelf-life testing at refrigeration temperatures, but fewer procedures have been

developed for the prediction of frozen storage temperatures. Since any extrapolation based on such

a narrow temperature range is fraught with uncertainty, the challenge becomes one of identifying

alternative information sources that can assist with the estimation of shelf-life, or accepting that the

initial evaluation of the temperature-dependent stability of a product will necessarily occupy

significant time (Reid et al., 2003). Ólafsdóttir et al. (1997) confirms that although, there are many

different physical measurements that provide information on parameters related to fish freshness,

none of these methods is able to determine unambiguously whether a fish is fresh or not.

In general, the lower the temperature of frozen storage, the lower the rate of microbiological

and biochemical changes. The article 5 of DL nº 37/2004 of February 26 describes that frozen

products should be kept at a temperature of -18 °C, or below, in all its points. Transportation and

sale admit the following maximum tolerances for temperature of frozen and deep-frozen products:

In the carriage: 3 °C;

In sales displays: 6 °C.

However, freezing and frozen storage do not inactivate enzymes and have a variable effect on

microorganisms (Fellows, 2000). Relatively high storage temperatures (between -4 °C and -10 °C)

have a greater lethal effect on microorganisms than do lower temperatures (between -15°C and -30

°C). Different types of microorganism also vary in their resistance to low temperatures; vegetative

cells of yeasts, moulds and gram negative bacteria (for example coliforms and Salmonella species)

are most easily destroyed; Gram-positive bacteria (for example Staphylococcus aureus and

Enterococci) and mould spores are more resistant, and bacterial spores (especially Bacillus species

and Clostridium species such as Clostridium botulinum) are virtually unaffected by low

temperatures. At normal frozen storage temperatures (-18 °C), there is a slow loss of quality owing

to both chemical changes and, in some foods, enzymatic activity. These changes are accelerated by

the high concentration of solutes surrounding the ice crystals, the reduction in water activity (to

0.82 at -20 °C in aqueous foods) and by changes in pH and redox potential. If enzymes are not

inactivated, the disruption of cell membranes by ice crystals allows them to react to a greater extent

with concentrated solutes. The main changes to frozen foods during storage are degradation of

pigments, loss of vitamins, residual enzyme activity, and oxidation of lipids (Fellows, 2000).


36


37

Part II – Experimental Work


38


39

Chapter 3. Materials and Methods

3.1. Fish preparation

Frozen and packaged Atlantic salmon fillet (Salmo salar) was obtained from a local

company (Vanibru – Comércio de Produtos Alimentares, Braga, Portugal). After unpacking, the

salmon fillets were cut into slices (samples) with the dimensions 10 cm × 5 cm × 2-3 cm (Figure

3.1) and an average weight of (113.4±7.4) g, using a vertical bone sawing machine (FK 32,

BIZERBA, Germany). This process was carried out in a refrigerated (~ 5 – 8 °C) room to minimize

temperature uptake. For each treatment, salmon samples (n=3) were individually packed in zip-lock

polyethylene freezer bags and stored in an industrial freezing chamber maintained at (-21.4±1.6)

°C, for 6 months.

Figure 3.1. Illustration of the salmon fillet, exemplifying the scheme of cuts used.

3.2. Preparation of the coating solutions

Chitosan from Golden-shell Biochemical Co. Ltd. (China) with a 91% degree of

deacetylation was used. The coating solutions were prepared by dissolving the chitosan (0.5% and


40

1.5%) in a 1% lactic acid solution with agitation, using a magnetic stirrer, at a temperature of 45

°C, until complete dissolution.

3.3. Preparation of the samples

3.3.1. Preparation of the samples coated with chitosan

The frozen salmon samples (-21.4±1.6) °C were weighted (W1), dipped in chitosan coating

solutions (5.18±0.49) °C for 0.5% chitosan solution and (8.10±0.57) °C for 1.5% chitosan

solution, the solution temperature was monitored by the probe (HANNA Instruments, HI765PW,

Romania) of the infrared Pronto Plus thermometer (HANNA Instruments, HI99556-10, Romania)).

The samples were dipped 35 seconds in 0.5% chitosan solution and 10 seconds in 1.5% chitosan

solution, drained for 2 minutes and weighted again (W2). These dives were performed in a pilot-scale

glazing tank (Figure 3.2) with the help of a stainless steel mesh, who collected the samples from

inside the tank, in order to minimize the interference with the amount of coating applied. Following

the Equation 2, the coating uptake was calculated, where W1 and W2 indicate the weight of the

salmon sample before and after the coating application, respectively. An average of (9.6±0.1)% and

(10.0±0.2)% of coating uptake (wt%) was obtained for the chitosan solutions of 0.5% and 1.5%,

respectively.

Equation 2

Figure 3.2. Pilot-scale glazing tank (left) and dipping instrument – mesh (right) constructed in A151 316 stainless steel.


41

3.3.2. Preparation of the samples glazed with water

A similar process was followed for the salmon samples glazed. These samples were

weighted (W3), dipped in water (0.28±0.08) °C for 40 s, drained for 1 minute and weighted again

(W4). The glazing uptake obtained was calculated by Equation 3, where W3 and W4 indicate the

weight before and after the glaze is apply in the samples, respectively. An average of glazing uptake

of (8.4±0.3)% was obtained.

Equation 3

3.3.3. Preparation of the control samples

The samples that have integrated the control group were left untreated. These non-coated

samples were used for comparison with the remaining groups of samples.

3.4. Storage and transport of the samples

All salmon samples were individually packed in zip-lock polyethylene freezer bags, inside

corrugated boxes, and stored in an industrial freezing chamber maintained at (-21.4±1.6) °C, for 6

months. This temperature was monitored using a data logger (DS7922 1Wire® Thermochrom®

iButton®, Dallas Semiconductor Inc., U.S.A.).

Salmon samples were then transported to the laboratory in polystyrene boxes with an

appropriate quantity of ice accumulators. Once in the laboratory, the salmon samples were kept in

a common freezer at ~20 °C until further use. Fish samples were taken from each package, and

microbiological and physicochemical analyses were performed at regular intervals. All analysis was

done in triplicate.


42

3.5. Samples Analysis

3.5.1. Coating Loss

After the storage period, the coated samples were weighed again (W5) and the coating loss

was determined using Equation 4.

Equation 4

3.5.2. Glazing Loss

After the storage period, the glazed samples were weighed again (W6) and the glazing loss

was determined with Equation 5.

Equation 5

3.5.3. Weight Loss

The control samples left untreated don’t have any coating. In this case, the uncoated

samples were initially weighed (W7) and after the storage period were weighed again (W8) and the

weight loss determined with Equation 6:

Equation 6

3.5.4. Drip Loss

To calculate Drip Loss, all the frozen samples were removed from the freezer, kept for 22

hours in the refrigerator at 5 °C, removed from the zip-lock polyethylene bag and placed on a rack

for 2 minutes to release drip, and the thawed samples were weighed. The Drip Loss was

determined by Equation 7, were W9 indicates the weight of frozen samples without coating/glazing


43

and before being placed in the refrigerator; W10 indicate the weight of thawed samples (Sathivel et

al., 2007).

Equation 7

3.5.5. Determination of TVC

Total Aerobic Plate counts were estimated by the procedure based on the BS EN ISO

4833:2003 Standard Protocol. A 25 g sub-sample of product was required for testing. Since the

package should be cleaned and sanitized before opening, the external surface of rigid or semi-rigid

packages of fish was cleaned with detergent and water ensuring no contamination of the package

contents occurs. The opening of the package was carried out using sterile scalpels, scissors or

forceps. All operations during and after opening were carried out under aseptic conditions without

interruption.

The testing sample was obtained removing randomly -selected individual sub-samples, each

of not more than 1 g mass to produce an appropriate mass of sample for testing. The test sample

was added to a stomacher bag containing 9 volumes of maximum recovery diluent (MRD) and

stomached for 1 minute. Using a fresh sterile pipette 1mL of the initial inoculum was transferred

into 9 mL of MRD and the procedure was repeated for as many decimal dilutions as required

(Figure 3.3). The dilutions were mixed using a vortex mixer for 5 to 10 seconds. From each dilution,

1 mL of the initial inoculum was aseptically inoculated into a labeled Petri dish.

In parallel, a boiling bath melted a 500 mL pre-sterilized bottle of Plate Count Agar (PCA) in

60-90 minutes. Bottles were warmed slightly before placing in very hot water (to avoid cracking),

taking care not to overheat the agar. After melting, the agar was left on the bench for 20 minutes to

ensure that the bottle will not crack and then it was placed in a water bath of temperature (46±1)

°C (a single bottle took roughly 1 hour to cool to 46 °C).

Subsequently, 15 mL of tempered (46±1) °C PCA agar was added to each Petri dish by

mixing and swirling six times clockwise, six times left to right, six times anticlockwise and six times

up and down (the time elapsing between the preparation of the initial suspension and contact with

the agar did not exceed 45 minutes). After solidifying, were inverted and placed into an incubator at

(30±1) °C for (72±3) hours.


44

The colonies from each Petri dish containing more than 15 and fewer than 300 colonies

were counted and the number of microorganisms (N) present in the test sample was calculated

using Equation 8, where ∑c is the sum of colonies counted, n1 is the number of dishes retained in

the first dilution, n2 is the number of dishes retained in the second dilution and d is the dilution

factor corresponding to the first dilution. The results were reported as the number of

microorganisms per gram of sample.

The Total Viable Counts (TVC) were estimated for samples frozen (-20 °C) and thawed

samples (5.9 °C).

Equation 8

Source: adapted from http://sciencelane.com/?p=689, consulted in 13/08/2013

Figure 3.3. Example of serial dilution from an initial sample.


45

3.5.6. Determination of TBA

TBA (2-thiobarbituric acid) was determined by spectrophotometric detection according to

the standard procedure described in NP 3356:2009.

A portion of 15 g of sample was accurately weighed on an analytical balance (Mettler

AE200) and homogenized in a blender (BECKEN coffee grinder, Worten, Portugal) at least twice. A

solution of trichloroacetic acid was prepared by adding 75 g of trichloroacetic acid, 1 g of EDTA

(ethylenediaminetetraacetic acid Titriplex II), 20 mL of an alcoholic solution of propyl gallate 5% into

a 1000 mL volumetric flask, making up the volume with distilled water and homogenizing. The

milled sample was placed into a 50 mL falcon tube and a 30 mL of solution of trichloroacetic acid

7.5% was added with a pipette. This solution waited about 2 minutes for the extraction was

complete. Then, the extract was filtered with filter paper Whatmann nº 1 for beaker, obtaining a

clear extract. Next, it was accurately pipetted to a test tube 5 mL of extract and 5 mL of 0.02 M

thiobarbituric acid (TBA). The test tube was capped and placed in a boiling water bath (~100°C)

(VWR, VMS-C7 Advanced magnetic hotplate stirrer supplied with a glass coated PT1000 probe) for

40 minutes. After this time, the tubes were removed from the bath and cooled under cold running

water, opened carefully and stirred to avoid the formation of air bubbles. The content of each tube

was transferred to a quartz cell of 10 mm and the absorbance was measured with a

spectrophotometer (Jasco V-560 UV/Vis spectrophotometer, Japan) at 530 nm, using distilled

water in the reference cell. A reagent blank was run at the same time. This reagent blank was

produced under the same conditions, but replacing the volume of extract for an equal volume of

distilled water. The TBA index is given in the amount of malondialdehyde, expressed in mg per

1000 g of sample (mg MDA/Kg sample), according to Equation 9, where C is the concentration of

malondialdehyde, expressed in micromoles; v is the volume, in milliliters of the extract; H is the

moisture content of the sample, expressed as a percentage; m is the mass, in grams, of the taking

the test (Appendix A1).

Equation 9


46

3.5.7. Determination of TVB-N

The value of TVB-N was determined by the method of Conway as set out in NP 2930:2009.

A 50 g sample (ms) was homogenized with 100 mL of 5% trichloroacetic acid and, after 2 minutes,

the mixture was filtered through gauze. Then, it was transferred 1 mL of boric acid (H3BO3) to the

center of the Conway cell (Figure 3.4). On the periphery of the Conway cell was added 1 mL of

filtrate (V3), 0.5 mL distilled water and 1 mL of potassium carbonate (K2CO3) saturated solution. The

Conway cell was carefully closed without stirring the solutions and placed in an incubator at 40 °C

for 90 minutes. After that period, the boric acid was titrated with 0.02 mol/L hydrochloric acid, until

a pink coloration was acquired. A blank and a diffusion control were performed by replacing the

volume of extract by an equal volume of distilled water and 0.1% ammonium sulfate, ((NH4)2SO4)

respectively (Appendix A2). The TVB-N value was determined according to the Equation 10, where

V0, V1 and V2 represent the volumes of hydrochloric acid (mL) added in the blank test, in the

diffusion control test, and in the extract test, respectively, and Fc is a volume correction factor

(moisture of sample). The results were expressed in mg of nitrogen per 100 g of sample (mg N/100

g sample).

Equation 10

Source: adapted from http://www.ufrgs.br/imunovet/molecular_immunology/invitrocellfree.html and

http://www.inchem.org/documents/antidote/antidote/ant02.htm, consulted in 14/08/2013

Figure 3.4. Representation of the Conway cell.


47

3.5.8. Determination of K-value

The K-value was determined according to the method of Ryder (1985) as described by

Souza et al. (2010). A 5 g of sample was homogenized (BECKEN coffee grinder, Worten, Portugal)

with 25 mL of chilled 0.6 mol/L perchloric acid (HClO4) at 0 °C for 1 minute. The homogenate was

centrifuged (EBA 20, Hettich zentrifugen, Germany) at 3000×g (6000 rpm) for 10 minutes. Using a

pH meter, 10 mL of the supernatant was adjusted to pH 6.5-6.8 with 1 mol/L potassium hydroxide

(KOH) (Metrohm 620 pH meter, Swiss made). After standing at flaked ice for 30 min, the

potassium perchlorate that precipitated was removed by filtration using a Whatman nº 1 filter paper.

The filtrate was diluted to 20 mL with Milli-Q purified distilled water, passed through a 0.20 µm

Fioroni membrane, and stored at -80 °C until the subsequent analyses.

Twenty microliter aliquots of all samples were analyzed using a HPLC (Hitachi High-

Technologies Corporation chromatograph (VWR, Tokyo, Japan)) equipped with a Organizer (Elite

Lachrom), Pump (Elite Lachrom L-2130), UV-Vis detector (Elite Lachrom L-2420) at 254 nm,

Autosampler (Elite Lachrom L-2200) and Column oven (Elite Lachrom L-2300) with a

Purospher®Star RP-18e (endcapped particles, 5 µm particle size, LichroCART® 250-4 HPLC

Cartridge, ART. 1.50252.0001, Sorbent Lot Hx947476, Merck, Germany) column (Figure 3.5).

Separation of the nucleotide products was achieved using a mobile phase of 0.04 mol/L potassium

dihydrogen orthophosphate (KH2PO4) and 0.06 mol/L dipotassium hydrogen orthophosphate

(K2HPO4) dissolved in 1:1 ratio in Milli-Q purified distilled water, at a flow rate of 1 mL/min. The

peaks obtained from fish muscle extracts were identified and quantified through standard solution

curves (Appendix A3). ATP breakdown products comprising ATP, ADP, AMP, IMP, HxR, and Hx were

measured, and the K value was calculated using Equation 11 described by Saito et al. (1959):

Equation 11


48

Figure 3.5. Organizational scheme of the HPLC equipment used.

3.5.9. Determination of pH-value

After removing the coating/glazing with a knife in order to prevent changes in the samples, a 5

g portion of each sample was homogenized with 50 mL of Milli-Q purified distilled water in a mixer

(BECKEN coffee grinder, Worten, Portugal) for 30 s and the pH value of the suspension was

measured using a pH meter (Metrohm 620 pH meter, Swiss made) (Figure 3.6) (Fan et al., 2009;

Sathivel et al., 2007; Souza et al., 2010).


49

Source: adapted from http://www.quimica.seed.pr.gov.br/modules/galeria/detalhe.php?foto=1488&evento=4 and http://www.pet-

tabs.us/freshwater_quality/category/ph, consulted in 14/05/2013

Figure 3.6. Illustration of the pH meter and pH range typical for freshwater fish.

3.5.10. Determination of color parameters

The instrumental measurement of color salmon was performed using a colorimeter

(CHROMA METER CR-400/410, AQUATEKNICA, SA, Konica Minolta, Japan). The results were

expressed using the CIE L*a*b* system. The parameters measured were the luminosity L* (L*=0 for

black and L*=100 for white) and color coordinates a* (-a* for green and +a* for red) and b* (-b* for

blue and +b* for yellow). The salmon samples presented, to the "naked eye", a similar color for the

same groups, i.e. for the control group (uncoated), the glazed group, the group coated with 0.5%

chitosan and the group coated with 1.5% chitosan, the samples appeared identical colors. The

values obtained for the first trial served as a reference standard. These values were discounted to

the values of the following tests to calculate the ∆E*ab. The samples, with approximately 1 cm of

thickness, were evaluated at six different points, 3 points on the right side and three points on the

left side to obtain an average value that minimizes the color difference within the same sample. The

equipment was calibrated using the white calibration plate (Figure 3.7). Salmon samples were


50

stored in a controlled temperature chamber at (-15.8±1.7) °C and they were taken at different time

points for evaluation (Appendix A4). This temperature was monitored using a data logger (DS7922

1-Wire® Thermochrom® iButton®, Dallas Semiconductor Inc., U.S.A.).

Figure 3.7. Illustration of the methodology and equipment used in the measurement of the color of salmon.

3.5.11. Statistical analysis

All experiments were performed in triplicate. The mean values of those 3 independent

determinations were calculated for each treatment at every moment. The statistical significance of

differences among treatment means was evaluated by analysis of variance (ANOVA) followed by the

Tukey test with significance at p<0.05. Data were evaluated statistically using the software

STATISTICA version 7.0 (StatSoft Inc. 2004).


51


4.1. Coating Loss

The effect of chitosan coatings, water glazing and storage time on the coating/glazing loss

of salmon samples during storage at -22 °C is shown in Figure 4.1.

The application of both coatings, chitosan coating and water glazing, presented no

statistically significant effect in the initial coating/glazing loss values. Although stable in the first

moments, after 13 and 39 days of storage, where the glazed samples presented a coating loss of

(2.64±0.73)% and (2.47±0.93)% and the samples coated with 0.5% chitosan showed a coating

loss of (2.09±0.96)% and (1.80±1.17)%, the samples coated with 0.5% chitosan showed a

tendency to have smaller values than the glazed samples. This tendency was evident in the two

subsequent periods, after 68 and 125 days of storage, when this difference becomes significantly

different. In fact, the glazed samples presented a coating loss of (4.72±1.22)% against

(1.91±0.83)% for the 0.5% chitosan coated samples, after 68 days of storage. After 125 days of

storage, the glazed samples showed a coating loss of (3.43±1.17)% against (1.29±0.48)% for the

samples coated with 0.5% chitosan. Such decrease was also observed in the last moment, after

182 days of storage, although not significantly different was found, since the size of the deviation

standard. However, it is possible to detect an effect more pronounced and effective of the 0.5%

chitosan coating in the control of water transfer of the samples when compared with the samples

glazed with water.

Samples coated with 1.5% chitosan were also tested on two occasions, 68 days and 182

days of storage. Though these samples did not show significant differences compared to the other

coatings, they appear to lose less coating than the glazed samples. These samples presented a

coating loss of (3.19±0.89)% against (4.72±1.22)% for the glazed samples, after 68 days of storage

and a coating loss of (2.49±1.61)% compared with the glazing loss of (3.36±0.53)%, after 182 days

of storage. The same tendency did not occur for samples coated with 0.5% chitosan, where

samples coated with 1.5% chitosan seem to suffer a greater coating loss. The samples coated with

1.5% chitosan lost (3.19±0.89)% against the coating loss of (1.91±0.83)% for the samples coated

with 0.5% chitosan, after 68 days of storage. After 182 days of storage, a coating loss of

(2.49±1.61)% for the samples coated with 1.5% chitosan still was higher than (2.37±1.37)% to

samples coated with 0.5% chitosan.


52

In matter of fact and according to Gonçalves et al. (2009), the weight loss by dehydration

during freezing and storage can be reduced by both methods: covering the surface with packaging

material or surrounding the product with a thin layer of ice. If, on one hand, Jacobsen & Fossan

(2001) argue that if the product is subject to inadequate cold storage, the glaze will evaporate

instead of the tissue water itself, on the other hand, Kilincceker et al. (2009) claims that a barrier is

formed against the water oozing out, with the process of coating, conserving the majority of the

water inside the product. However, Kester & Fennema (1986), cited by Sathivel et al. (2007) and

Rodriguez-Turienzo et al. (2011), reported that chitosan coatings may function as moisture-

sacrificing agents instead of moisture barriers, thus moisture loss from the product could be

delayed until the moisture contained within the chitosan coating had been evaporated. That is,

while coatings loose their water by sublimation during storage, they prevent losses of food moisture.

A study performed by Soares et al. (2013) reports that coating loss from frozen salmon

stored at -5 °C increases during storage. The different storage temperatures may explain the

different results, since at -5 °C ice is closer to its melting point and more liquid water is available

than at -18 °C. The apparent stability of the coating loss values for the different coatings indicate

that an adequate freezing temperature (< -18 °C) can be effective in reducing coating loss during

storage and by so increase fish protection.

Figure 4.1. Coating Loss (%) for salmon samples glazed with water ( Q0) and coated with 0.5% chitosan ( Q5) and 1.5% chitosan ( Q15) during 6 months of storage at -22 °C. Each bar represents the mean ± standard deviation of three replications. Different small letters in the same

0%

1%

2%

3%

4%

5%

6%

7%

13 39 68 125 182

Coa

ting

Loss

(%)

Storage time at -22°C (days)


53

day and different capital letters in bars with the same color indicate a statistically significant difference (Tukey test, p<0.05).

4.2. Weight Loss

The weight loss of salmon samples during storage at -22 °C is shown in Figure 4.2. These

values showed no significant differences throughout storage. However, despite being very small,

these values show an increasing tendency along the entire storage, starting at (0.08±0.04)% and

ending at (0.16±0.07)%.

Johnston et al. (1994) states that weight loss due to dehydration in a freezer depends on

the type of freezer, freezing time, type of product, air velocity and freezer operating conditions.

These reduced values might be explained by a well controlled storage temperature, since the

temperature profile from the industrial chamber used showed an amplitude of temperature values

less than 2 °C (Appendix A4 – Figure A.11) and due to the fact that all samples, including the

control samples without coating, are stored in polyethylene bags, inside corrugated boxes, which

also act as protection.

Figure 4.2. Weight Loss (%) of uncoated salmon samples from the control group ( QS) during 6 months of storage at -22 °C. Each bar represents the mean ± standard deviation of three replications. Different letters indicate a statistically significant difference (Tukey test, p<0.05).

0,00%

0,05%

0,10%

0,15%

0,20%

0,25%

13 39 68 125 182

Wei

ght L

oss

(%)



54

4.3. Drip Loss

The drip loss of salmon samples during storage at -22 °C is shown in Figure 4.3. The

different coatings did not appear to interfere in a significant way to the drip loss, since the values do

not show significant statistical differences The initial drip loss value for the control sample, without

coating, was (1.7±0.5)%. The values for these control samples increased over the storage from

(0.5±0.2)%, passing through (1.7±0.1)% and (2.3±1.5)%, to (4.8±1.0)% at the end, after 40, 69,

126 and 183 days of storage, respectively. Except for the 126 days of storage, all coatings also

increased the amount of drip loss. The glazed samples started with a drip loss of (1.7±0.3)%, after

40 days of storage and (2.3±0.3)% after 69 days of storage, ending with (5.0±0.6)% after 183 days

of storage. The samples coating with 0.5% of chitosan passed by (0.9±0.4)%, (3.1±0.3)%,

(4.3±0.5)%, after 40, 69, 183 days of storage, respectively. Also the samples coating with 1.5% of

chitosan increased their values from (2.2±1.1)% to (3.6±0.2)%, after 69 and 183 days of storage.

A study performed by Sathivel et al. (2007) showed drip losses of (0.5±0.3)% for uncoated

salmon samples, (2.9 ± 1.2)% for glazed samples and (6.1±0.9)% for chitosan coated samples,

after 8 months of frozen storage at -35 °C. Mackie (1993) cited in Sathivel et al. (2007) reported

that myosin aggregation in frozen fish fillets during storage leads to muscle toughening and drip loss

during thawing. Drip loss is also dependent on thawing temperature and rate of thawing.

As a whole, drip loss followed a growing trend during storage, increasing significantly to

almost twice, in the last moment, for all samples. According to Fellows (2000), temperature

fluctuation has a cumulative effect on food quality. During thawing, in samples subjected to slow

freezing or recrystallisation, cells do not regain their original shape and turgidity because the

growing ice crystals deform and rupture adjacent cell walls, increasing the release of cell

constituents (water-soluble nutrients) to form drip losses. Very high freezing rates may also cause

stresses within some foods that result in splitting or cracking of the tissues. Furthermore, drip losses

form substrates for enzyme activity and microbial growth.


55

Figure 4.3. Drip Loss (%) of salmon samples from the control group ( QS), glazed with water ( Q0) and coated with 0.5% chitosan ( Q5) and 1.5% chitosan ( Q15) during 6 months of storage at -22 °C. Each bar represents the mean ± standard deviation of three replications. Different small letters in the same day and different capital letters in bars with the same color indicate a statistically significant difference (Tukey test, p<0.05).

4.4. TVC

The total viable counts (TVC) values for frozen salmon samples (-20 °C) during 6 months of

storage at -22 °C are presented in Table 4.1. Based upon these data it is possible to notice that

uncoated samples showed values constantly greater than glazed samples, except for the last

moment when the value of the glaze sample ((3.65E+04±5.15E+04) CFU/g) is greater than the

value of the control sample ((2.97E+03±2.65E+03) CFU/g ). Both of these values are greatly

affected by the standard deviations due to the reduced number of concordant repetitions. In turn,

the samples coated with chitosan showed favorable values of TVC when compared with the

uncoated samples or the glazed samples. While samples coated with 1.5% chitosan constantly

show values below 10, the same did not happen with samples coated with 0.5% chitosan. In fact,

the samples coated with 0.5% chitosan presented a TVC value of 1.50E+02, 2.10E+02, 2.60E+02

CFU/g, after 40, 118 and 181 days of storage, for one of the samples belonging to the same

triplicate. Both coatings - water and chitosan - acted in the reduction and maintenance of the

microbial composition of the frozen samples. However, the samples coated with chitosan showed

the most promising results in microbial protection of frozen fishery products, especially the samples

0%

1%

2%

3%

4%

5%

6%

7%

40 69 126 183

Drip

Los

s (%

)



56

coated with 1.5% chitosan. This ability of chitosan coatings to reduce, inhibit or prevent growth of

microorganisms on food surfaces has been referenced by several authors, including Falguera et al.

(2011) and Pereira et al. (2010).

Freezing also seems to have been effective, since all TVC values, including for uncoated

samples, are below the acceptable threshold around 10E+07―10E+08 CFU/g, which lies at the

point of sensory rejection (Ólafsdóttir et al., 1997) and never exceeded the microbiological limit of

5E+05 CFU/g recommended by ICMSF (1986) for frozen fish of good quality.

Table 4.1. Total viable counts (TVC) values for frozen salmon samples (-20 °C) from the control group ( QS), glazed with water ( Q0) and coated with 0.5% chitosan ( Q5) and 1.5% chitosan ( Q15) after 6 months of storage at -22 °C; standard deviation corresponds to three replications

TVC (-20 °C)

Storage Time (days)

Sample 1 (CFU/g)

Sample 2 (CFU/g)

Sample 3 (CFU/g)

Mean (CFU/g)

SD

QS

0 6.00E+03 5.80E+02 1.60E+03 2.73E+03 2.88E+03

13 3.60E+02 1.50E+02 5.80E+02 3.63E+02 2.15E+02 40 1.00E+03 1.70E+03 5.40E+02 1.08E+03 5.84E+02

62 <10 <10 <10 <10 - 118 5.10E+02 2.70E+02 6.40E+02 4.73E+02 1.88E+02

181 1.10E+03 1.80E+03 6.00E+03 2.97E+03 2.65E+03

Q0

13 7.90E+02 7.20E+02 5.60E+02 6.90E+02 1.18E+02 40 5.80E+02 1.20E+03 3.50E+02 7.10E+02 4.40E+02

62 3.50E+02 3.30E+02 1.20E+02 2.67E+02 1.27E+02 118 4.80E+02 2.10E+02 7.80E+02 4.90E+02 2.85E+02

181 9.60E+04 7.00E+03 6.60E+03 3.65E+04 5.15E+04

Q5

13 <10 <10 <10 <10 - 40 <10 1.50E+02 <10 - -

62 <10 <10 <10 <10 - 118 2.10E+02 <10 <10 - -

181 2.60E+02 <10 <10 - -

Q15 62 <10 <10 <10 <10 -

181 <10 <10 <10 <10 -

The total viable counts (TVC) values for the unfrozen salmon samples (5.9 °C) after 24 h,

during 6 months of storage at -22 °C are presented in Table 4.2. After analyzing these data, the

same trend observed for frozen salmon could be confirmed. Again, all values are below the

threshold of rejection and uncoated samples have higher TVC values than glazed and coated

samples. The coated samples show TVC values lower than glazed samples. However, all coatings,

especially chitosan coatings, are effective in protecting the thawed samples at refrigeration


57

temperatures. Coatings with 1.5% chitosan again showed consistent protection of the samples, this

time thawed, maintaining the TVC below 10 in all samples, throughout the entire storage period.

This way, 1.5% chitosan coatings demonstrated to be effective in protecting thawed samples at

refrigerated temperatures, simulating the thawing conditions of fish at consumers' homes.

Table 4.2. Total viable counts (TVC) values for refrigerated salmon samples (5.9 °C) from the control group ( QS), glazed with water ( Q0) and coated with 0.5% chitosan ( Q5) and 1.5% chitosan ( Q15) after 6 months of storage at -22 °C; standard deviation corresponds to three replications

TVC (5.9 °C)

Storage Time (days)

Sample 1 (CFU/g)

Sample 2 (CCFU/g)

Sample 3 (CFU/g)

Mean (CFU/g)

SD

QS

0 7.30E+03 2.00E+03 3.30E+03 4.20E+03 2.76E+03 13 2.80E+03 2.20E+02 1.50E+03 1.51E+03 1.29E+03

40 4.50E+03 9.50E+03 7.10E+03 7.03E+03 2.50E+03 62 1.50E+02 1.40E+02 1.00E+02 1.30E+02 2.65E+01

118 2.80E+05 3.00E+02 1.20E+03 9.38E+04 1.61E+05

181 1.10E+05 3.70E+03 1.00E+04 4.12E+04 5.96E+04

Q0

13 1.10E+03 1.10E+03 1.40E+03 1.20E+03 1.73E+02

40 1.60E+03 1.40E+03 8.70E+02 1.29E+03 3.77E+02 62 7.10E+02 3.70E+02 1.40E+03 8.27E+02 5.25E+02

118 1.20E+03 3.20E+03 3.70E+03 2.70E+03 1.32E+03

181 8.60E+04 7.60E+03 8.50E+03 3.40E+04 4.50E+04

Q5

13 <10 1.00E+02 2.50E+02 - -

40 <10 2.40E+02 1.50E+02 - - 62 <10 <10 <10 <10 -

118 3.20E+02 3.00E+02 <10 - -

181 1.40E+03 1.50E+02 8.40E+02 7.97E+02 6.26E+02

Q15 62 <10 <10 <10 <10 -

181 <10 <10 <10 <10 -

4.5. TBA

Thiobarbituric acid (TBA) values for frozen salmon samples during storage are shown in

Figure 4.4. The initial TBA value for the control uncoated sample was (0.2234±0.0305) mg

MDA/kg sample. Although TBA values are very similar, for 39 days of storage, the samples have

higher TBA values as (0.3179±0.0462), (0.2541±0.0219), (0.3948±0.0550) mg MDA/kg sample,

for the uncoated, glazed and 0.5% chitosan coated samples, respectively. This may have been due


58

to TBA test limitations, such as lack of sensitivity and specificity of the spectrophotometric method,

since many other substances may react with the TBA reagent (Shahidi & Zhong, 2005).

A study conducted by Sathivel et al. (2007), using the same method, began with similar

TBA value for the uncoated sample with (0.25 ±0.08) mg MDA/kg fish. However, after 8 months of

storage at -35 °C, the TBA value of the control sample increased to (7.4±1.4) mg MDA/kg fish,

while the glazed sample presented a TBA value of (1.8±0.5) mg MDA/kg fish and the sample

coated with 1% chitosan displays a TBA value of (1.3±0.6) mg MDA/kg fish. This study concluded

that distilled water and 1% chitosan coatings were effective in reducing lipid oxidation in salmon

fillets, when compared with uncoated samples. The same conclusion can not be drawn by

analyzing the TBA values of the present work, since all samples appear to have a certain stability,

except for 39 days of storage. In fact, there was no visible influence of different coatings in the

control of lipid oxidation, which is confirmed by the absence of statistically significant differences.

The freezing process seems to have been the major factor influencing lipid oxidation and the

storage time may not have been long enough to show a clear difference.

Figure 4.4. Thiobarbituric acid (TBA) values for salmon samples from the control group ( QS), glazed with water ( Q0) and coated with 0.5% chitosan ( Q5) and 1.5% chitosan ( Q15) during 5 months of storage at -22 °C. Each bar represents the mean ± standard deviation of three replications. Different small letters in the same day and different capital letters in bars with the same color indicate a statistically significant difference (Tukey test, p<0.05).

0,00

0,05

0,10

0,15

0,20

0,25

0,30

0,35

0,40

0,45

0,50

13 39 69 124

TBA

(mg

MD

A/Kg

sam

ple)



59

4.6. TVB-N

The TVB-N values for frozen salmon samples during storage are presented in Figure 4.5.

The initial TVB-N value for the uncoated sample was (6.96±1.01) mg nitrogen/100 g sample. In the

first moments, after 13 and 69 days, the TVB-N values suffered no great changes, decreasing

slightly. The uncoated samples decreased from (5.82±0.87) to (4.81±0.01), the glazed samples

from (4.43±0.08) to (4.30±0.01) and the samples coated with 0.5% chitosan fell from (5.63±0.63)

to (5.14±0.82) nitrogen/100 g sample. However, all the samples increased at the last moment,

after 188 days of storage for (6.02±1.09), (8.45±1.69) and (6.66±1.12) nitrogen/100 g sample,

for the uncoated, glazed and 0.5% chitosan coated samples, respectively. The same is true for the

samples coated with 1.5% chitosan, who increased from (4.29±0.02) to (7.27±1.37) nitrogen/100

g sample, after 69 and 188 days of storage, respectively.

In general, the TVB-N values are quite similar for all the coatings, contributing to the lack of

statistically significant differences. Such lack of significant differences did not allow the detection of

any contribution by the different coatings. These low values, far below the 35 mg nitrogen/100 g

fish established as limit of acceptability of salmon by Directive 95/149/EC, indicate a good state of

fish preservation. The increase of TVB-N values, in the last moment, might have been due to the

activity of spoilage bacteria and endogenous enzymes, which are slowed down at low temperatures.

Thus, if the experiments were maintained, it would be expected that degradation becomes clearer. It

is also important to note that the storage temperature was a relevant factor, being able to inhibit

changes in volatile compounds and consequently increasing the TVB-N, after 6 months of frozen

storage.


60

Figure 4.5. Total volatile basic nitrogen (TVB-N) values for salmon samples from the control group ( QS), glazed with water ( Q0) and coated with 0.5% chitosan ( Q5) and 1.5% chitosan ( Q15) during 6 months of storage at -22 °C. Each bar represents the mean ± standard deviation of three replications. Different small letters in the same day and different capital letters in bars with te same color indicate a statistically significant difference (Tukey test, p<0.05).

4.7. K-value

The K-values for frozen salmon samples, during 6 months of storage, are presented in

Figure 4.6. From the data analysis it is not possible to draw any conclusions about the effect of

coatings and glaze in ATP degradation of salmon samples, since the results are very similar and no

statistically significant difference was detected. The initial K-value for the uncoated sample was

(90.47±5.05)%. The K-values for the first moments, after 14 and 41 days of storage, were very

high, with all above 80%. For the remaining time, after 69, 126 and 182 days of storage, the K-

values are lower, never exceeding 67%. This can be explained by the occurrence of some error in

the first moments that may have compromised these values, which appeared more reasonable over

the latest times. Nevertheless, it was expected that the samples could exhibit high K-value results,

since they came from a processed product with source in aquaculture.

According to Erikson et al. (1997), it seems reasonable to propose an upper K-value limit of

70 to 80% for good-quality Atlantic salmon (ice stored), and tentatively, lower than 40 to 50% for

excellent quality. Disregarding the values of the two initial moments, it can be stated that the

0

2

4

6

8

10

12

13 69 188

TVB

-N (m

g N

/100

g s

ampl

e)



61

remaining salmon samples indicate a fish of good quality, since they are below the maximum

rejection limit of 80%.

Again the storage temperature emerges as an important factor in the stabilization of K-

values. According to the study conducted by Soares et al. (2013), for 14 weeks of storage at -5 °C,

the K-values showed an increasing trend, which did not happen in this experiment at -22 °C, where

the K-values returned quite similar.

Figure 4.6. K-values for salmon samples from the control group ( QS), glazed with water ( Q0) and coated with 0.5% chitosan ( Q5) and 1.5% chitosan ( Q15) during 6 months of storage at -22 °C. Each bar represents the mean ± standard deviation of three replications. Different small letters in the same day and different capital letters in bars with the same color indicate a statistically significant difference (Tukey test, p<0.05).

4.8. pH-value

pH values during frozen storage are represented in Figure 4.7. The initial pH of the

uncoated sample was found to be (6.43±0.05). After 14 days, the pH of all samples increased to

(6.82±0.03), (6.85±0.09) and (6.85±0.05) for the uncoated, glazed and 0.5% chitosan coated

samples, respectively. Throughout the remaining storage time, the pH values show a significant

decreasing trend, which is supported by the results of the statistical analysis (capital letters).

According to Rodriguez-Turienzo et al. (2011), freezing cause changes in pH values of fish muscle

0%

20%

40%

60%

80%

100%

120%

14 41 69 126 182

K-v

alue

(%)


― Maximum Rejection Limit


62

towards higher acidity, probably due to the increase in concentration of substances in the water that

remains unfrozen and modifies the acid–base equilibrium. The mean pH of all the samples were

not higher than the limit of 6-6.5, recommended by Varlık et al. (1993) and Gülyavuz & Ünlüsayın

(1999) cited in Kilincceker et al. (2009), with the exception for the first moment (13 days).

Similar results were reported in a study conducted by Sathivel et al. (2007) during 8

months at -35 °C. The uncoated samples showed a pH of (6.6±0.1), glazed samples a pH of

(6.5±0.1) and samples coated with chitosan a pH of (6.4±0.1). In this case, the effect of pH on the

coatings was unimportant, since did not vary significantly.

Figure 4.7. pH values for salmon samples from the control group ( QS), glazed with water ( Q0) and coated with 0.5% chitosan ( Q5) and 1.5% chitosan ( Q15) during 6 months of storage at -22 °C. Each bar represents the mean ± standard deviation of three replications. Different small letters in the same day and different capital letters in bars with the same color indicate a statistically significant difference (Tukey test, p<0.05).

4.9. Color parameters

As is commonly known salmon has natural color variations. For this reason, it was not

possible to compare the color match between different coatings, since each sample group was

obtained from different salmon fillets. These variations would greatly affect the color parameters

during storage, which would invalidate any conclusions. Therefore, it was only evaluated the

5,5

5,7

5,9

6,1

6,3

6,5

6,7

6,9

7,1

13 40 69 126 182

pH


― Recommended Limit


63

variation of the color parameters for each group of samples - uncoated, glazed and coated samples

- over time, separately.

The color parameters for the samples uncoated, glazed, coated with 0.5% chitosan and

1.5% chitosan are presented in Figure 4.8, Figure 4.9, Figure 4.10 and Figure 4.11, respectively.

The temperature profile of the freezing chamber which contained the samples during storage is

visible in Appendix A4 – Figure A.12.

The results for L*a*b* obtained during the experiment did not present significantly

variations or any kind of tendency. Probably, a longer period of time may show more significant

results.

Figure 4.12 shows the variation in perceived color differences of the salmon samples during

storage (∆E*ab). By the graphical analysis is noticeable a large variability of the values, especially

for the samples coated with 0.5% chitosan. This may have been due to a colorimeter reading error

of the first sample coated with 0.5% chitosan, which serves as a standard for the remaining time.

The samples coated with 1.5% chitosan were those that showed the most promising results. During

the storage, these samples showed greater stability, presenting, for the majority of time, the lowest

values of ∆E*ab, even below the acceptable thresholds of 1.1 to 2.1 (Ahmad, 2006). For this

reason, this coating may be the one who better protects fish color, since this was the coating who

caused minor color differences when applied on frozen salmon samples, perhaps being

imperceptible and better accept by consumers. According to Rodriguez-Turienzo et al. (2011), the

type of plasticizer does not affect the color of frozen samples, since it is possible that the structure

of the coating was different (perhaps more homogeneous) when it was applied on a frozen surface.

In fact, when coating is applied after freezing, might better protect the carotenoids against oxidation.

Moreover, carotenoids are bound to some myofibrillar proteins, thus the degree of protein

denaturation also influences color changes (Ojagh et al., 2011 cited in Rodriguez-Turienzo et al.,

2011).


64

Figure 4.8. Color parameters for salmon samples from the control group for the coordinates L* ( QSL*), a* ( QSa*) and b* ( QSb*) during 3.5 months of storage at -18 °C. Each bar represents the mean ± standard deviation of three replications. Different letters in the same color coordinate indicate a statistically significant difference (Tukey test, p<0.05).

Figure 4.9. Color parameters for salmon samples from the group glazed with water for the coordinates L* ( Q0L*), a* ( Q0a*) and b* ( Q0b*) during 3.5 months of storage at -18 °C. Each bar represents the mean ± standard deviation of three replications. Different letters in the same color coordinate indicate a statistically significant difference (Tukey test, p<0.05).

0

10

20

30

40

50

60

0 14 28 41 50 70 86 103

L*a*

b*

Storage Time at -18°C (days)

0

10

20

30

40

50

60

0 14 28 41 50 70 86 103

L*a*

b*



65

Figure 4.10. Color parameters for salmon samples from the group coated with 0.5% of chitosan for the coordinates L* ( Q5L*), a* ( Q5a*) and b* ( Q5b*) during 3.5 months of storage at -18 °C. Each bar represents the mean ± standard deviation of three replications. Different letters in the same color coordinate indicate a statistically significant difference (Tukey test, p<0.05).

Figure 4.11. Color parameters for salmon samples from the group coated with 1.5% of chitosan for the coordinates L* ( Q15L*), a* ( Q15a*) and b* ( Q15b*) during 3.5 months of storage at -18 °C. Each bar represents the mean ± standard deviation of three replications. Different letters in the same color coordinate indicate a statistically significant difference (Tukey test, p<0.05).

0

10

20

30

40

50

60

0 14 28 41 50 70 86 103

L*a*

b*


0

10

20

30

40

50

60

70

0 14 28 41 50 70 86 103

L*a*

b*



66

Figure 4.12. ∆E*ab values for salmon samples from the control group ( QS), glazed with water ( Q0) and coated with 0.5% chitosan ( Q5) and 1.5% chitosan ( Q15) during 3,5 months of storage at -18 °C.

0

2

4

6

8

10

12

14

14 28 41 50 70 86 103

∆E*

ab


― Maximum Acceptance threshold


67

Chapter 5. Conclusions and future perspectives

Introducing chitosan coatings as an alternative to water glazing, usually carried out at

industrial level represented a challenge. To prove their reliability a series of analysis were performed

for evaluated the stability of the different parameters that affect fish quality during storage.

Parameters such as coating loss, weight loss, drip loss, TVC, TBA, TVB-N, K-value, pH and L*a*b*

coordinates give information about the freshness of the salmon samples, indicating the state of

superficial dehydration, microbiological contamination, lipid oxidation, protein denaturation, and

changes in odor and color.

Coating loss showed that water glazed samples loose more glazing when compared with the

other samples. Both chitosan coatings present better results than the glaze with water. The

apparent stability of the coating loss values for the different coatings indicates that an adequate

freezing temperature can be effective in fish protection. However, the most promising results have

been given by coating with 0.5% chitosan, which show the lowest values of coating loss during

storage. Thus, this coating may be the one that best protects the samples against temperature

fluctuations and other harmful factors during prolonged freezing.

Weight loss appears to have a growing trend during the storage; the values obtained were

very low. This may help to conclude that one effective control of the storage prevents moisture loss

by sublimation due to temperature fluctuations.

Regarding drip loss, the values show no significant influence of the different coatings on the

test progress. As a whole, drip loss seems to increase at the last moment probably because of the

accumulated damage caused by recrystallization and temperature fluctuations. However, given the

slow progression of the increase in drip loss it is likely that the correct storage of the samples

delayed their deterioration.

TVC results demonstrate that different coatings acted in the control of microbial flora.

However, chitosan coatings, in particular the 1.5% chitosan coating, are those that return best

results. Both chitosan coatings showed to be effective in protecting frozen and thawed samples

(after 24 hours). The samples coated with 1.5% chitosan were even able to maintain the same level

of protection in frozen and thawed samples. So, these coatings act as an additional barrier to

overcome the contamination of salmon, thus improving the microbiological safety of salmon fillets


68

during storage. Also freezing appears to be useful in controlling microbial growth, since all samples,

including the uncoated samples, remain below the acceptable limit of TVC during the entire storage.

The low TBA values allow concluding about product stability, where the different coatings do

not appear to have a role; this may be due to the fact that freezing temperatures stabilize lipid

oxidation. On the other hand, these low values may imply that a longer storage time would be

needed in order to see some effects of coating usage.

TVB-N tests returned quite stable, indicating a fish in good condition. In fact, the low TVB-N

values were far below the limit of acceptability established for salmon. Since the different coatings

do not seem to affect this parameter, the stability suggests an efficiency of freezing in the

maintenance of TVB-N values.

K-values show that the effect of glazing and coatings on the ATP degradation of the salmon

samples is not relevant. Moreover, the statistic differences do not support any conclusion. However,

in general, the samples indicate a fish of good quality, since the maximum rejection limit was not

exceeded. Again the storage temperature emerges as an important factor in the stabilization of K-

values.

pH-values show a decreasing trend. The effect of pH on the coatings was unimportant,

since did not vary significantly. The mean pH of all the samples is not higher than the

recommended limit.

Due to the color data stability no clear differences were found in the statistical analysis

during storage. Regarding the ∆E*ab values, the 1.5% chitosan coating was the one with best

results maintaining, after 103 days, the color closer to the initial salmon and below values that can

be considered acceptable for consumers.

One of the main conclusions of this work is the confirmation of the importance of a proper

freezing and the relevance of storage control. Indeed, various parameters such as the coating loss,

weight loss, drip loss, TVC, TBA, TVB-N, and K-value returned very stable due to the protection

provided by a correct freezing temperature and a suitable control of its maintenance. Another

important finding was the effective protection of the 1.5% chitosan coating in maintaining the color

of salmon and in the control of microbial activity. In fact, the samples coated with 1.5% chitosan

showed less perceptible color differences and provided a consistent protection for both - frozen and

thawed samples - against microbiological contamination. Thus, this coating is a viable alternative to

water glaze, not affecting the perception of quality of consumers.


69

In conclusion, edible coatings together with a correct freezing and storage control not only

help in retarding the growth of microorganisms, but also help in the maintenance of chemical

constituents, therefore reducing superficial dehydration, lipid oxidation, protein denaturation and

changes in odor and color.

In the future, it is proposed to continue color analyses, for a longer period of time, as well

as the use of sensory methods that complement the information given by the physical, chemical

and micro(biological) parameters presented in this work. A study that enables to define more

precisely the amount of coating applied in order to be able to realize the influence of coating

thickness on samples’ weight loss, can equally be very useful. It would also be appropriate to

assess the migration of edible coating for frozen fish and the insertion of nanoencapsulated

bioactive compounds in these same coatings.

An economic analysis to assess the viability of an effective industrially application of

chitosan as a substitute for glazing would be relevant to a greater visibility of this study. Keeping this

in mind, the costs should reveal the higher cost of chitosan solutions in relation to water, the need

for a high concentration and its greater thickness which would require a large investment in

adaptation of industrial equipment already existent. Also take into account, the need of lower

uptakes and temperature maintenance and a smaller dive time, which may represent an energy

and time savings.


70


71

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75

Appendixes

A1 – Standard Curve Determination for TBA method

Table A.1. Schematic representation of Standard Curve Determination for TBA method Calibration TBA (mL) TCA (mL)* TEP (mL) MDA (µmol) Abs**

Blank 5 5 - - - P1 5 0 5 0,05 1

P2 5 1 4 0,04 0,8 P3 5 2 3 0,03 0,6

P4 5 3 2 0,02 0,4 P5 5 4 1 0,01 0,2

Source: adapted from Lemon (1975) *Extraction solution (Working Standard Solution) **Approximate Resulting Absorbance

Figure A.1. Color grading obtained on the calibration curve.


76

Figure A.2. Calibration curve for the TBA method.

Absorbance= (77,08±10,64)[TEP] - (0,0251±0,0706) R² = 0,9944

0,0

0,1

0,2

0,3

0,4

0,5

0,6

0,7

0,8

0,9

0 0,002 0,004 0,006 0,008 0,01 0,012

Abso

rban

ce

[TEP] (μmol/mL)


77

A2 – Illustrations assistants to the implementation of standard NP2930:2009

Source: adapted from NP 2930:2009

Figure A.3. Scheme illustrates the preparation of the various tests on Conway cell.

Source: adapted from NP 2930:2009

Figure A.4. Representation of the alkalization by the action of potassium carbonate to release volatile bases and their reception in a boric acid solution followed by titration, in the interior of Conway cell.


78


79

A3 – Calibration curves for HPLC determinations

Table A.2. Scheme followed to obtain the calibration curves for K-value method

Standards Concentration (μmol/mL) The mother solution was made from the mixture of 6 compounds: IMP, ATP, ADP, AMP, Hx and HxR. The standards are obtained from dilutions of the mother solution.

“Mother solution” 2,00 1 1,00 2 0,75 3 0,60 4 0,30 5 0,15 6 0,05

Figure A.5. Calibration curve of IMP.

Area = 6E+07[IMP] + 3E+06 R² = 0,9886

0,0E+00

2,0E+07

4,0E+07

6,0E+07

8,0E+07

1,0E+08

1,2E+08

1,4E+08

0 0,5 1 1,5 2 2,5

Area

[IMP] (μmol/mL)


80

Figure A.6. Calibration curve of ATP.

Figure A.7. Calibration curve of ADP.

Area= 6E+07[ATP] + 2E+06 R² = 0,9939

0,0E+00

2,0E+07

4,0E+07

6,0E+07

8,0E+07

1,0E+08

1,2E+08

1,4E+08

0 0,5 1 1,5 2 2,5

Area

[ATP] (μmol/mL)

Area= 6E+07[ADP] + 1E+06 R² = 0,9971

0,0E+00

2,0E+07

4,0E+07

6,0E+07

8,0E+07

1,0E+08

1,2E+08

1,4E+08

0 0,5 1 1,5 2 2,5

Area

[ADP] (μmol/mL)


81

Figure A.8. Calibration curve of Hx.

Figure A.9. Calibration curve of AMP.

Area= 6E+07[Hx] + 571191 R² = 0,9938

0,0E+00

2,0E+07

4,0E+07

6,0E+07

8,0E+07

1,0E+08

1,2E+08

0 0,5 1 1,5 2 2,5

Area

[Hx] (μmol/mL)

Area= 8E+07[AMP] + 962578 R² = 0,9976

0,0E+00

2,0E+07

4,0E+07

6,0E+07

8,0E+07

1,0E+08

1,2E+08

1,4E+08

1,6E+08

0 0,5 1 1,5 2 2,5

Area

[AMP] (μmol/mL)


82

Figure A.10. Calibration curve of HxR.

Area= 6E+07[HxR] + 167555 R² = 0,9979

0,0E+00

2,0E+07

4,0E+07

6,0E+07

8,0E+07

1,0E+08

1,2E+08

1,4E+08

0 0,5 1 1,5 2 2,5

Area

[HxR] (μmol/mL)


83

A4 – Measurement of freezing chamber temperature during storage

Figure A.11. Air temperature of industrial freezing chamber registered every 10 minutes by a data logger during frozen storage.

Figure A.12. Air temperature of freezing chamber registered every 10 minutes by a data logger during frozen storage.

-30

-25

-20

-15

-10

-5

0 Te

mpe

ratu

e (°

C)

-25

-20

-15

-10

-5

0

5

10

Tem

pera

ture

(°C

)

Marina Soraia Gomes de Oliveira - Universidade do Minho · em frente, vivendo dias iguais de forma...

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